技术领域Technical Field
本发明涉及信息安全防护技术领域，尤其涉及一种基于人工智能的金融信息安全防护方法及系统。The present invention relates to the field of information security protection technology, and in particular to a financial information security protection method and system based on artificial intelligence.
背景技术Background Art
随着金融科技的迅猛发展，金融机构逐渐依赖多模态数据进行决策和运营，这些数据包括文本、语音、图像、用户个人信息以及交易记录等不同形式的信息，这些多模态金融信息在带来业务创新的同时，也引发了新的安全挑战，传统的加密和防护技术在应对复杂多样的金融信息安全威胁时显得力不从心，特别是在面对日益复杂的网络攻击时，现有技术难以提供足够的防护，当前的金融信息安全体系主要依赖于静态的加密算法和预定义的安全策略，这些方法在面对未知或变种攻击时，往往无法及时调整，导致安全防护出现漏洞，此外，随着攻击技术的不断进化，攻击者开始利用高级的攻击策略，如利用人工智能生成对抗样本，从而绕过传统的安全防护机制，而现有的金融信息安全防护不够灵活、智能，无法面对不断变化的威胁环境，从而导致金融信息被攻击。With the rapid development of financial technology, financial institutions have gradually relied on multimodal data for decision-making and operations. These data include text, voice, images, user personal information, transaction records and other forms of information. While these multimodal financial information bring business innovation, they also trigger new security challenges. Traditional encryption and protection technologies are powerless in dealing with complex and diverse financial information security threats, especially in the face of increasingly complex network attacks. Existing technologies are difficult to provide adequate protection. The current financial information security system mainly relies on static encryption algorithms and predefined security policies. These methods often cannot be adjusted in time when facing unknown or variant attacks, resulting in security loopholes. In addition, with the continuous evolution of attack technology, attackers have begun to use advanced attack strategies, such as using artificial intelligence to generate adversarial samples, thereby bypassing traditional security protection mechanisms. Existing financial information security protection is not flexible and intelligent enough to face the ever-changing threat environment, resulting in financial information being attacked.
发明内容Summary of the invention
基于此，有必要提供一种基于人工智能的金融信息安全防护方法及系统，以解决至少一个上述技术问题。Based on this, it is necessary to provide an artificial intelligence-based financial information security protection method and system to solve at least one of the above-mentioned technical problems.
为实现上述目的，一种基于人工智能的金融信息安全防护方法，包括以下步骤：To achieve the above purpose, a financial information security protection method based on artificial intelligence includes the following steps:
步骤S1：获取多模态金融信息；对多模态金融信息进行模态类型识别，并进行信息块分组，得到金融交易行为信息块和用户基础信息块；Step S1: Acquire multimodal financial information; identify the modal type of the multimodal financial information and group the information blocks to obtain a financial transaction behavior information block and a user basic information block;
步骤S2：对金融交易行为信息块进行行为模式分析，并进行行为趋势预测，生成预测行为数据；Step S2: Analyze the behavior pattern of the financial transaction behavior information block, predict the behavior trend, and generate predicted behavior data;
步骤S3：基于预测行为数据对用户基础信息块进行层次关联映射，得到用户行为共识数据；Step S3: Based on the predicted behavior data, hierarchical association mapping is performed on the user basic information blocks to obtain user behavior consensus data;
步骤S4：利用用户行为共识数据对多模态金融信息进行动态联合加密，生成加密金融信息；Step S4: dynamically jointly encrypt multimodal financial information using user behavior consensus data to generate encrypted financial information;
步骤S5：基于预测行为数据对加密金融信息进行漏洞攻击模拟，并进行安全威胁筛查，得到安全威胁数据；Step S5: simulating vulnerability attacks on encrypted financial information based on the predicted behavior data, and performing security threat screening to obtain security threat data;
步骤S6：基于安全威胁数据对加密金融信息进行二次加密，生成重加密金融信息。Step S6: re-encrypt the encrypted financial information based on the security threat data to generate re-encrypted financial information.
本发明通过对多模态金融信息进行模态类型识别和信息块分组，有效地将复杂的多维数据进行分类和结构化处理，提高了数据分析与处理的效率与准确性，通过对金融交易行为进行模式分析和趋势预测，能够提前识别潜在的风险行为，生成预测行为数据，有助于提前采取防护措施，利用用户行为共识数据进行动态联合加密，使加密策略能够根据实时行为数据进行调整，从而增强了加密的灵活性和针对性，提升了信息的安全性，基于预测行为数据进行漏洞攻击模拟与安全威胁筛查，可以有效识别潜在的安全威胁，提供及时的风险预警和防护，通过在初次加密后基于安全威胁数据进行二次加密，进一步加强了金融信息的安全保护，防止多种类型的攻击，确保信息的完整性和机密性。The present invention effectively classifies and structures complex multi-dimensional data by performing modal type identification and information block grouping on multi-modal financial information, thereby improving the efficiency and accuracy of data analysis and processing. By performing pattern analysis and trend prediction on financial transaction behaviors, it is possible to identify potential risk behaviors in advance and generate predicted behavior data, which is helpful to take protective measures in advance. Dynamic joint encryption is performed using user behavior consensus data, so that encryption strategies can be adjusted according to real-time behavior data, thereby enhancing the flexibility and pertinence of encryption and improving the security of information. Vulnerability attack simulation and security threat screening based on predicted behavior data can effectively identify potential security threats and provide timely risk warnings and protection. Secondary encryption based on security threat data after initial encryption further strengthens the security protection of financial information, prevents various types of attacks, and ensures the integrity and confidentiality of information.
本发明还提供一种基于人工智能的金融信息安全防护系统，用于执行如上所述的基于人工智能的金融信息安全防护方法，该基于人工智能的金融信息安全防护系统包括：The present invention also provides an artificial intelligence-based financial information security protection system, which is used to execute the artificial intelligence-based financial information security protection method as described above. The artificial intelligence-based financial information security protection system includes:
信息获取模块，用于获取多模态金融信息；对多模态金融信息进行模态类型识别，并进行信息块分组，得到金融交易行为信息块和用户基础信息块；The information acquisition module is used to acquire multimodal financial information; identify the modal type of the multimodal financial information, and group the information blocks to obtain the financial transaction behavior information block and the user basic information block;
行为预测模块，用于对金融交易行为信息块进行行为模式分析，并进行行为趋势预测，生成预测行为数据；The behavior prediction module is used to analyze the behavior patterns of the financial transaction behavior information blocks, predict the behavior trends, and generate predicted behavior data;
信息映射模块，用于基于预测行为数据对用户基础信息块进行层次关联映射，得到用户行为共识数据；The information mapping module is used to perform hierarchical association mapping on the user basic information blocks based on the predicted behavior data to obtain the user behavior consensus data;
联合加密模块，用于利用用户行为共识数据对多模态金融信息进行动态联合加密，生成加密金融信息；Joint encryption module, used to dynamically joint encrypt multimodal financial information using user behavior consensus data to generate encrypted financial information;
威胁筛查模块，用于基于预测行为数据对加密金融信息进行漏洞攻击模拟，并进行安全威胁筛查，得到安全威胁数据；A threat screening module is used to simulate vulnerability attacks on encrypted financial information based on predicted behavior data, and to perform security threat screening to obtain security threat data;
二次加密模块，用于基于安全威胁数据对加密金融信息进行二次加密，生成重加密金融信息。The secondary encryption module is used to perform secondary encryption on encrypted financial information based on security threat data to generate re-encrypted financial information.
本发明通过利用信息获取模块能够识别和分类不同类型的多模态金融信息，将复杂的金融数据分割为行为信息块和基础信息块，增强了系统在处理多维数据时的精度与效率，行为预测模块通过分析金融交易行为模式，进行趋势预测，提前识别潜在风险和异常行为，提升了系统的前瞻性与响应能力，信息映射模块利用预测行为数据进行层次关联映射，生成用户行为共识数据，使得用户基础信息与行为信息之间的关联更为紧密和精准，为后续的加密和防护提供了坚实基础，联合加密模块通过动态联合加密技术，根据用户行为共识数据实时调整加密策略，使得加密过程更加灵活，能够有效应对不断变化的安全威胁，威胁筛查模块通过模拟漏洞攻击并进行安全威胁筛查，及时发现加密信息中的潜在威胁，有助于提前识别和预防安全漏洞，增强系统的整体防护能力，二次加密模块在初次加密的基础上，结合安全威胁数据进行二次加密，进一步提升了金融信息的保密性和安全性，确保在面对复杂的攻击时仍能保持高强度的防护。The present invention can identify and classify different types of multimodal financial information by utilizing the information acquisition module, divide complex financial data into behavioral information blocks and basic information blocks, and enhance the accuracy and efficiency of the system in processing multidimensional data. The behavioral prediction module analyzes the financial transaction behavior pattern, performs trend prediction, and identifies potential risks and abnormal behaviors in advance, thereby improving the system's foresight and responsiveness. The information mapping module uses the predicted behavior data to perform hierarchical association mapping and generates user behavior consensus data, so that the association between user basic information and behavior information is closer and more accurate, providing a solid foundation for subsequent encryption and protection. The joint encryption module uses dynamic joint encryption technology to adjust the encryption strategy in real time according to the user behavior consensus data, making the encryption process more flexible and able to effectively respond to changing security threats. The threat screening module simulates vulnerability attacks and performs security threat screening to timely discover potential threats in encrypted information, which helps to identify and prevent security vulnerabilities in advance and enhance the overall protection capability of the system. The secondary encryption module performs secondary encryption in combination with security threat data on the basis of the initial encryption, further improving the confidentiality and security of financial information, and ensuring that high-intensity protection can be maintained in the face of complex attacks.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为一种基于人工智能的金融信息安全防护方法的步骤流程示意图；FIG1 is a schematic diagram of the steps of a financial information security protection method based on artificial intelligence;
图2为图1中步骤S2的详细实施步骤流程示意图；FIG2 is a schematic diagram of a detailed implementation process of step S2 in FIG1 ;
图3为图1中步骤S3的详细实施步骤流程示意图；FIG3 is a schematic diagram of a detailed implementation process of step S3 in FIG1 ;
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.
具体实施方式DETAILED DESCRIPTION
下面结合附图对本发明的技术方法进行清楚、完整的描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域所属的技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.
此外，附图仅为本发明的示意性图解，并非一定是按比例绘制。图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器方法和/或微控制器方法中实现这些功能实体。In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor methods and/or microcontroller methods.
应当理解的是，虽然在这里可能使用了术语“第一”、“第二”等等来描述各个单元，但是这些单元不应当受这些术语限制。使用这些术语仅仅是为了将一个单元与另一个单元进行区分。举例来说，在不背离示例性实施例的范围的情况下，第一单元可以被称为第二单元，并且类似地第二单元可以被称为第一单元。这里所使用的术语“和/或”包括其中一个或更多所列出的相关联项目的任意和所有组合。It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and/or" used herein includes any and all combinations of one or more of the listed associated items.
为实现上述目的，请参阅图1至图3，一种基于人工智能的金融信息安全防护方法，包括以下步骤：To achieve the above purpose, please refer to Figures 1 to 3, a financial information security protection method based on artificial intelligence includes the following steps:
步骤S1：获取多模态金融信息；对多模态金融信息进行模态类型识别，并进行信息块分组，得到金融交易行为信息块和用户基础信息块；Step S1: Acquire multimodal financial information; identify the modal type of the multimodal financial information and group the information blocks to obtain a financial transaction behavior information block and a user basic information block;
步骤S2：对金融交易行为信息块进行行为模式分析，并进行行为趋势预测，生成预测行为数据；Step S2: Analyze the behavior pattern of the financial transaction behavior information block, predict the behavior trend, and generate predicted behavior data;
步骤S3：基于预测行为数据对用户基础信息块进行层次关联映射，得到用户行为共识数据；Step S3: Based on the predicted behavior data, hierarchical association mapping is performed on the user basic information blocks to obtain user behavior consensus data;
步骤S4：利用用户行为共识数据对多模态金融信息进行动态联合加密，生成加密金融信息；Step S4: dynamically jointly encrypt multimodal financial information using user behavior consensus data to generate encrypted financial information;
步骤S5：基于预测行为数据对加密金融信息进行漏洞攻击模拟，并进行安全威胁筛查，得到安全威胁数据；Step S5: simulating vulnerability attacks on encrypted financial information based on the predicted behavior data, and performing security threat screening to obtain security threat data;
步骤S6：基于安全威胁数据对加密金融信息进行二次加密，生成重加密金融信息。Step S6: re-encrypt the encrypted financial information based on the security threat data to generate re-encrypted financial information.
本发明通过对多模态金融信息进行模态类型识别和信息块分组，有效地将复杂的多维数据进行分类和结构化处理，提高了数据分析与处理的效率与准确性，通过对金融交易行为进行模式分析和趋势预测，能够提前识别潜在的风险行为，生成预测行为数据，有助于提前采取防护措施，利用用户行为共识数据进行动态联合加密，使加密策略能够根据实时行为数据进行调整，从而增强了加密的灵活性和针对性，提升了信息的安全性，基于预测行为数据进行漏洞攻击模拟与安全威胁筛查，可以有效识别潜在的安全威胁，提供及时的风险预警和防护，通过在初次加密后基于安全威胁数据进行二次加密，进一步加强了金融信息的安全保护，防止多种类型的攻击，确保信息的完整性和机密性。The present invention effectively classifies and structures complex multi-dimensional data by performing modal type identification and information block grouping on multi-modal financial information, thereby improving the efficiency and accuracy of data analysis and processing. By performing pattern analysis and trend prediction on financial transaction behaviors, it is possible to identify potential risk behaviors in advance and generate predicted behavior data, which is helpful to take protective measures in advance. Dynamic joint encryption is performed using user behavior consensus data, so that encryption strategies can be adjusted according to real-time behavior data, thereby enhancing the flexibility and pertinence of encryption and improving the security of information. Vulnerability attack simulation and security threat screening based on predicted behavior data can effectively identify potential security threats and provide timely risk warnings and protection. Secondary encryption based on security threat data after initial encryption further strengthens the security protection of financial information, prevents various types of attacks, and ensures the integrity and confidentiality of information.
本发明实施例中，参阅图1，为本发明一种基于人工智能的金融信息安全防护方法的步骤流程示意图，在本实例中，所述一种基于人工智能的金融信息安全防护方法包括以下步骤：In the embodiment of the present invention, referring to FIG1, it is a schematic diagram of the steps of a financial information security protection method based on artificial intelligence of the present invention. In this example, the financial information security protection method based on artificial intelligence includes the following steps:
步骤S1：获取多模态金融信息；对多模态金融信息进行模态类型识别，并进行信息块分组，得到金融交易行为信息块和用户基础信息块；Step S1: Acquire multimodal financial information; identify the modal type of the multimodal financial information and group the information blocks to obtain a financial transaction behavior information block and a user basic information block;
本实施例中，获取多模态金融信息，并对其进行模态类型识别和信息块分组。通过部署在金融机构的多个数据采集终端或系统，收集不同模态的金融信息，包括但不限于文本数据、图像数据、音频数据以及视频数据。利用卷积神经网络（CNN）对多模态数据进行模态类型识别，确定每种数据的具体类别，如将交易记录归类为文本数据，将客户身份识别数据归类为图像数据。通过基于聚类算法的分组方法，将同一模态类型的信息整合成对应的信息块，从而形成金融交易行为信息块与用户基础信息块两大类数据集合。In this embodiment, multimodal financial information is obtained, and modal type identification and information block grouping are performed. Financial information of different modalities is collected through multiple data collection terminals or systems deployed in financial institutions, including but not limited to text data, image data, audio data, and video data. Convolutional neural networks (CNNs) are used to identify the modal type of multimodal data and determine the specific category of each data, such as classifying transaction records as text data and classifying customer identity data as image data. Through a grouping method based on a clustering algorithm, information of the same modality type is integrated into corresponding information blocks, thereby forming two major data sets: financial transaction behavior information blocks and user basic information blocks.
步骤S2：对金融交易行为信息块进行行为模式分析，并进行行为趋势预测，生成预测行为数据；Step S2: Analyze the behavior pattern of the financial transaction behavior information block, predict the behavior trend, and generate predicted behavior data;
本实施例中，对金融交易行为信息块进行行为模式分析，并进行行为趋势预测，生成预测行为数据。首先使用时间序列分析算法（如LSTM网络）对金融交易行为信息块中的历史交易数据进行建模，捕捉其中的行为模式，包括常见的交易频率、交易金额、交易时间段特征。基于识别出的模式，使用预测模型如GRU网络进行行为趋势预测，输出未来的交易行为趋势。其中趋势数据不仅包括常规的交易活动预测，还涵盖潜在的异常交易行为，如大额资金转移或多次跨境交易，从而生成精确的预测行为数据。In this embodiment, the behavior pattern analysis is performed on the financial transaction behavior information block, and the behavior trend prediction is performed to generate predicted behavior data. First, the historical transaction data in the financial transaction behavior information block is modeled using a time series analysis algorithm (such as an LSTM network) to capture the behavior patterns therein, including common transaction frequencies, transaction amounts, and transaction time period characteristics. Based on the identified patterns, a prediction model such as a GRU network is used to predict the behavior trend and output future transaction behavior trends. The trend data includes not only conventional transaction activity predictions, but also potential abnormal transaction behaviors, such as large-scale fund transfers or multiple cross-border transactions, thereby generating accurate predicted behavior data.
步骤S3：基于预测行为数据对用户基础信息块进行层次关联映射，得到用户行为共识数据；Step S3: Based on the predicted behavior data, hierarchical association mapping is performed on the user basic information blocks to obtain user behavior consensus data;
本实施例中，基于预测行为数据对用户基础信息块进行层次关联映射，得到用户行为共识数据。采用基于图数据库的关联分析技术，将预测行为数据与用户基础信息块进行多维度的关联映射。具体包括构建用户信息节点与行为数据节点，通过用户ID、交易账户等关键标识符建立节点之间的关联边。然后，利用深度学习算法（如图卷积网络，GCN），对映射后的数据进行层次化处理，识别出用户与其行为模式之间的隐性关联，生成一个以用户为中心的行为共识数据集合。反映了每个用户在特定预测行为情境下的整体行为特征。In this embodiment, the user basic information block is hierarchically associated and mapped based on the predicted behavior data to obtain the user behavior consensus data. The predicted behavior data and the user basic information block are multi-dimensionally associated and mapped using the association analysis technology based on the graph database. Specifically, it includes constructing user information nodes and behavior data nodes, and establishing association edges between nodes through key identifiers such as user ID and transaction account. Then, a deep learning algorithm (such as the convolutional network, GCN) is used to hierarchically process the mapped data, identify the implicit association between the user and his behavior pattern, and generate a user-centered behavior consensus data set. It reflects the overall behavioral characteristics of each user in a specific predicted behavior context.
步骤S4：利用用户行为共识数据对多模态金融信息进行动态联合加密，生成加密金融信息；Step S4: dynamically jointly encrypt multimodal financial information using user behavior consensus data to generate encrypted financial information;
本实施例中，利用用户行为共识数据对多模态金融信息进行动态联合加密，生成加密金融信息。根据用户行为共识数据提取出用户特定的加密密钥参数，这些参数与用户的行为模式密切相关。采用分布式密钥生成算法（如Shamir秘密共享方案），将多模态金融信息的加密密钥动态分散到各个用户行为共识数据中。通过对称加密算法（如AES-256）结合行为共识密钥参数，对整个多模态金融信息块进行联合加密处理，确保数据在存储和传输中的高度安全性，最终生成加密的金融信息数据。In this embodiment, user behavior consensus data is used to dynamically jointly encrypt multimodal financial information to generate encrypted financial information. User-specific encryption key parameters are extracted based on user behavior consensus data, and these parameters are closely related to the user's behavior pattern. A distributed key generation algorithm (such as the Shamir secret sharing scheme) is used to dynamically disperse the encryption key of multimodal financial information into each user behavior consensus data. The entire multimodal financial information block is jointly encrypted by a symmetric encryption algorithm (such as AES-256) combined with the behavior consensus key parameters to ensure high security of data in storage and transmission, and finally generate encrypted financial information data.
步骤S5：基于预测行为数据对加密金融信息进行漏洞攻击模拟，并进行安全威胁筛查，得到安全威胁数据；Step S5: simulating vulnerability attacks on encrypted financial information based on the predicted behavior data, and performing security threat screening to obtain security threat data;
本实施例中，基于预测行为数据对加密金融信息进行漏洞攻击模拟，并进行安全威胁筛查，得到安全威胁数据。采用生成对抗网络（GAN）模拟潜在的攻击模式，通过对抗样本生成技术构建不同类型的攻击场景，如模拟中间人攻击、侧信道攻击等，针对加密金融信息进行漏洞测试。利用深度包检测技术（DPI）对生成的攻击数据进行全面筛查，识别潜在的安全威胁和漏洞，如加密算法中的弱点或密钥管理中的风险，生成安全威胁数据。经过进一步的分析后，将用于指导后续的防护措施。In this embodiment, vulnerability attack simulation is performed on encrypted financial information based on predicted behavior data, and security threat screening is performed to obtain security threat data. Generative adversarial networks (GAN) are used to simulate potential attack patterns, and different types of attack scenarios are constructed through adversarial sample generation technology, such as simulated man-in-the-middle attacks, side channel attacks, etc., to perform vulnerability testing on encrypted financial information. Deep packet inspection technology (DPI) is used to comprehensively screen the generated attack data, identify potential security threats and vulnerabilities, such as weaknesses in encryption algorithms or risks in key management, and generate security threat data. After further analysis, it will be used to guide subsequent protective measures.
步骤S6：基于安全威胁数据对加密金融信息进行二次加密，生成重加密金融信息。Step S6: re-encrypt the encrypted financial information based on the security threat data to generate re-encrypted financial information.
本实施例中，基于安全威胁数据对加密金融信息进行二次加密，生成重加密金融信息。对前一步骤中生成的安全威胁数据进行分类和权重评估，识别出最严重的安全风险。针对这些高风险区域，应用基于格理论的加密算法（如NTRU加密算法），对先前已经加密的金融信息进行二次加密处理。通过两次加密过程，金融信息的安全性得到了进一步的提升，尤其是在高风险的威胁区域，确保数据即使在受到高级别攻击时，仍能保持机密性和完整性，最终生成更加安全的重加密金融信息。In this embodiment, encrypted financial information is encrypted twice based on security threat data to generate re-encrypted financial information. The security threat data generated in the previous step is classified and weighted to identify the most serious security risks. For these high-risk areas, an encryption algorithm based on lattice theory (such as the NTRU encryption algorithm) is applied to perform a second encryption process on the previously encrypted financial information. Through the two encryption processes, the security of financial information is further improved, especially in high-risk threat areas, ensuring that the data can maintain confidentiality and integrity even when subjected to high-level attacks, and ultimately generating more secure re-encrypted financial information.
优选地，步骤S1包括：Preferably, step S1 comprises:
S11：获取多模态金融信息；对多模态金融信息进行特征提取，得到多维特征向量；S11: acquiring multimodal financial information; performing feature extraction on the multimodal financial information to obtain a multidimensional feature vector;
步骤S12：根据多维特征向量对多模态金融信息进行模态聚类分析，生成模态聚类结果；Step S12: performing modal clustering analysis on the multimodal financial information according to the multidimensional feature vector to generate a modal clustering result;
步骤S13：对模态聚类结果进行逐类特征识别，得到类别模态特征向量；Step S13: performing feature recognition on the modal clustering results class by class to obtain a class modal feature vector;
步骤S14：对类别模态特征向量进行模态类型识别，生成模态分类数据；Step S14: performing modal type identification on the category modal feature vector to generate modal classification data;
步骤S15：基于模态分类数据对多模态金融信息进行信息块分组，得到金融交易行为信息块和用户基础信息块。Step S15: Group the multimodal financial information into information blocks based on the modal classification data to obtain financial transaction behavior information blocks and user basic information blocks.
本发明通过获取多模态金融信息并进行特征提取，能够有效地将不同形式的数据（如文本、图像、声音等）转化为结构化的多维特征向量，有助于提升对金融信息的全面理解和分析能力，利用多维特征向量进行模态聚类分析，可以将具有相似特征的数据自动归为同一类别，使得信息处理更具条理性，从而提高了数据处理的效率和准确性，对模态聚类结果进行逐类特征识别，能够提取每个类别的模态特征向量，有助于深入理解每种模态的特征，从而提高对特定模式的检测和分类能力，通过对类别模态特征向量进行模态类型识别，能够生成更为准确的模态分类数据，有助于更好地识别和区分不同类型的模态信息，从而提高金融信息的安全防护水平，基于模态分类数据对多模态金融信息进行信息块分组，可以将信息分为金融交易行为信息块和用户基础信息块，能够将相关信息有序组织，从而支持更为有效的分析和决策，对金融信息进行全面、深入的分析和分类，有助于识别潜在的安全威胁和异常行为，从而提升金融信息系统的安全防护精准度，精确的模态聚类、特征识别和分类能力有助于减少误报和漏报现象，确保金融信息安全防护措施的有效性和可靠性。The present invention can effectively convert different forms of data (such as text, images, sounds, etc.) into structured multi-dimensional feature vectors by acquiring multi-modal financial information and performing feature extraction, which helps to improve the comprehensive understanding and analysis capabilities of financial information. By using multi-dimensional feature vectors for modal clustering analysis, data with similar features can be automatically classified into the same category, making information processing more organized, thereby improving the efficiency and accuracy of data processing. By performing feature recognition on the modal clustering results, the modal feature vector of each category can be extracted, which helps to deeply understand the characteristics of each modality, thereby improving the detection and classification capabilities of specific patterns. By performing modal type recognition on the category modal feature vectors, it can It can generate more accurate modal classification data, which helps to better identify and distinguish different types of modal information, thereby improving the security protection level of financial information. By grouping multimodal financial information into information blocks based on modal classification data, the information can be divided into financial transaction behavior information blocks and user basic information blocks, and the relevant information can be organized in an orderly manner to support more effective analysis and decision-making. Comprehensive and in-depth analysis and classification of financial information helps to identify potential security threats and abnormal behaviors, thereby improving the security protection accuracy of financial information systems. Accurate modal clustering, feature recognition and classification capabilities help reduce false alarms and missed alarms, and ensure the effectiveness and reliability of financial information security protection measures.
本实施例中，获取多模态金融信息，并对其进行特征提取，得到多维特征向量，通过配置在金融系统中的数据采集模块，实时获取包括文本（如交易记录）、图像（如客户身份证照片）、音频（如客户通话记录）及视频（如监控摄像头画面）等多模态金融信息。利用深度学习中的特征提取技术，如卷积神经网络（CNN）对图像数据进行处理，提取图像特征，对文本数据使用词嵌入技术（如Word2Vec或BERT）将文本转换为数值特征；对音频数据应用声纹识别模型提取音频特征，对视频数据利用时序卷积网络（TCN）进行时序特征提取。所有提取出的特征通过拼接操作形成多维特征向量，涵盖各模态的信息特征，根据多维特征向量对多模态金融信息进行模态聚类分析，生成模态聚类结果，采用基于K均值算法的聚类方法对多维特征向量进行分析，通过选择合适的K值确定聚类中心数量，对特征向量进行K均值聚类，将具有相似特征的多模态金融信息分组，每个簇代表了一个聚类结果，其中包含了特征相似的金融数据，对于每个聚类簇，计算簇内数据点的均值向量，并根据这些均值向量生成模态聚类结果，以便进一步分析和处理，对模态聚类结果进行逐类特征识别，得到类别模态特征向量，对每个模态聚类结果应用特征识别技术。采用支持向量机（SVM）或随机森林分类器，对聚类结果进行逐类特征识别，提取每个聚类的主要特征，形成类别特征向量。每个类别特征向量描述了该类金融信息的核心特征，例如，金融交易信息包括交易时间、金额等特征，用户基础信息包括用户身份识别数据等，通过上述类别特征向量，能够对每个聚类类别的特征进行详细识别和描述，对类别模态特征向量进行模态类型识别，生成模态分类数据，利用分类算法（如深度神经网络中的全连接层）对类别模态特征向量进行进一步处理。首先，将每个类别模态特征向量输入到训练好的分类模型中，进行模态类型识别。通过训练数据中的标签信息，模型能够将类别特征向量映射到具体的模态类型，例如，金融交易行为、用户基础信息等，生成模态分类数据，每个数据点对应于其特定的模态类型标识，基于模态分类数据对多模态金融信息进行信息块分组，得到金融交易行为信息块和用户基础信息块，使用模态分类数据对原始多模态金融信息进行分组处理，通过匹配模态分类数据中的模态类型标识，将相关信息整合成两个主要的信息块：金融交易行为信息块和用户基础信息块；金融交易行为信息块包括与交易活动相关的数据，如交易记录、金额、时间等；用户基础信息块包括与用户身份相关的数据，如用户个人资料、身份证信息。In this embodiment, multimodal financial information is obtained, and features are extracted to obtain a multidimensional feature vector. Through the data acquisition module configured in the financial system, multimodal financial information including text (such as transaction records), images (such as customer ID card photos), audio (such as customer call records) and video (such as surveillance camera images) is obtained in real time. Feature extraction technology in deep learning, such as convolutional neural network (CNN), is used to process image data and extract image features. Word embedding technology (such as Word2Vec or BERT) is used to convert text data into numerical features; voiceprint recognition model is used to extract audio features from audio data, and temporal convolutional network (TCN) is used to extract temporal features from video data. All extracted features are concatenated to form a multi-dimensional feature vector, which covers the information features of each mode. Based on the multi-dimensional feature vector, the multi-modal financial information is subjected to modal clustering analysis to generate modal clustering results. The multi-dimensional feature vector is analyzed by a clustering method based on the K-means algorithm. The number of cluster centers is determined by selecting an appropriate K value. The feature vector is subjected to K-means clustering to group multi-modal financial information with similar features. Each cluster represents a clustering result, which contains financial data with similar features. For each cluster cluster, the mean vector of the data points in the cluster is calculated, and the modal clustering results are generated based on these mean vectors for further analysis and processing. The modal clustering results are subjected to class-by-class feature recognition to obtain the class modal feature vector. The feature recognition technology is applied to each modal clustering result. The clustering results are subjected to class-by-class feature recognition using a support vector machine (SVM) or a random forest classifier to extract the main features of each cluster and form a class feature vector. Each category feature vector describes the core features of this type of financial information. For example, financial transaction information includes features such as transaction time and amount, and user basic information includes user identity recognition data. Through the above category feature vectors, the features of each cluster category can be identified and described in detail, the category modal feature vectors can be identified as modal types, and modal classification data can be generated. The category modal feature vectors can be further processed using classification algorithms (such as the fully connected layer in a deep neural network). First, each category modal feature vector is input into the trained classification model for modal type identification. Through the label information in the training data, the model can map the category feature vector to a specific modal type, such as financial transaction behavior, basic user information, etc., to generate modal classification data. Each data point corresponds to its specific modal type identifier. Based on the modal classification data, the multimodal financial information is grouped into information blocks to obtain financial transaction behavior information blocks and user basic information blocks. The modal classification data is used to group the original multimodal financial information. By matching the modal type identifier in the modal classification data, the relevant information is integrated into two main information blocks: the financial transaction behavior information block and the user basic information block. The financial transaction behavior information block includes data related to transaction activities, such as transaction records, amounts, time, etc. The user basic information block includes data related to the user identity, such as user profile and ID card information.
优选地，步骤S2包括：Preferably, step S2 comprises:
步骤S21：对金融交易行为信息块进行时序特征分解，得到时序特征序列；Step S21: Decomposing the financial transaction behavior information block into time series features to obtain a time series feature sequence;
步骤S22：根据时序特征序列对金融交易行为信息块进行子序列模式匹配，生成行为模式匹配集合；Step S22: performing subsequence pattern matching on the financial transaction behavior information block according to the time series feature sequence to generate a behavior pattern matching set;
步骤S23：对行为模式匹配集合进行中心点聚类处理，得到模式中心数据；Step S23: performing center point clustering processing on the behavior pattern matching set to obtain pattern center data;
步骤S24：对模式中心数据进行变化趋势分析，生成模式趋势数据；Step S24: Analyze the change trend of the pattern center data to generate pattern trend data;
步骤S25：基于模式趋势数据对金融交易行为信息块进行行为趋势预测，生成预测行为数据。Step S25: Predicting the behavior trend of the financial transaction behavior information block based on the pattern trend data to generate predicted behavior data.
本发明通过对金融交易行为信息块进行时序特征分解，可以提取详细的时序特征序列，能够揭示金融交易行为中的时间依赖性，为后续的行为分析奠定基础，利用时序特征序列进行子序列模式匹配，可以生成行为模式匹配集合，能够识别出相似的交易模式，帮助识别常见的行为模式或潜在的异常行为。对行为模式匹配集合进行中心点聚类处理，能够得到模式中心数据，有助于总结和归纳主要的行为模式，从而提高对交易行为的整体理解和分析能力。对模式中心数据进行变化趋势分析，可以生成模式趋势数据，能够揭示行为模式随时间变化的规律，有助于识别长期趋势和异常波动。基于模式趋势数据对金融交易行为信息块进行行为趋势预测，能够生成预测行为数据，能够对未来的金融交易行为进行预判，提供前瞻性的风险管理和决策支持。通过时序特征分解和行为趋势预测，可以实现对金融交易的实时监控和风险检测，有助于及时发现和应对潜在的金融风险和安全威胁。结合模式匹配和趋势预测，能够更准确地识别异常交易模式和潜在的欺诈行为，从而提高金融信息系统的安全防护水平。自动化的时序特征提取、模式匹配和趋势预测技术可以减少人工干预和操作负担，提高金融信息安全防护系统的工作效率和可靠性。The present invention can extract detailed time series feature sequences by decomposing the financial transaction behavior information block, reveal the time dependency in the financial transaction behavior, and lay the foundation for subsequent behavior analysis. The time series feature sequences are used to perform subsequence pattern matching, and a behavior pattern matching set can be generated, which can identify similar transaction patterns and help identify common behavior patterns or potential abnormal behaviors. The behavior pattern matching set is subjected to center point clustering processing, and pattern center data can be obtained, which is helpful to summarize and summarize the main behavior patterns, thereby improving the overall understanding and analysis ability of the transaction behavior. The change trend analysis of the pattern center data can generate pattern trend data, which can reveal the law of the behavior pattern changing over time, and help identify long-term trends and abnormal fluctuations. Based on the pattern trend data, the behavior trend prediction of the financial transaction behavior information block can generate predicted behavior data, which can predict the future financial transaction behavior and provide forward-looking risk management and decision support. Through time series feature decomposition and behavior trend prediction, real-time monitoring and risk detection of financial transactions can be achieved, which helps to timely discover and respond to potential financial risks and security threats. Combining pattern matching and trend prediction can more accurately identify abnormal transaction patterns and potential fraudulent behavior, thereby improving the security protection level of financial information systems. Automated time series feature extraction, pattern matching and trend prediction technology can reduce manual intervention and operational burden, and improve the efficiency and reliability of financial information security protection systems.
作为本发明的一个实例，参考图2所示，在本实例中所述步骤S2包括：As an example of the present invention, referring to FIG. 2 , in this example, step S2 includes:
步骤S21：对金融交易行为信息块进行时序特征分解，得到时序特征序列；Step S21: Decomposing the financial transaction behavior information block into time series features to obtain a time series feature sequence;
本实施例中，对金融交易行为信息块进行时序特征分解，得到时序特征序列，将金融交易行为信息块中的每一笔交易数据处理为时序数据，首先将交易记录按时间戳排序。采用长短期记忆网络（LSTM）对这些时序数据进行特征分解，提取时间序列中的重要特征，如交易频率、交易金额波动等，首先对交易数据进行归一化处理，然后将其输入到LSTM模型中，通过模型的多个隐藏层对时间序列进行特征提取，生成一系列的时序特征序列。In this embodiment, the time series feature decomposition is performed on the financial transaction behavior information block to obtain a time series feature sequence, and each transaction data in the financial transaction behavior information block is processed into time series data. First, the transaction records are sorted by timestamp. The long short-term memory network (LSTM) is used to perform feature decomposition on these time series data to extract important features in the time series, such as transaction frequency, transaction amount fluctuation, etc. The transaction data is first normalized and then input into the LSTM model. The time series is feature extracted through multiple hidden layers of the model to generate a series of time series feature sequences.
步骤S22：根据时序特征序列对金融交易行为信息块进行子序列模式匹配，生成行为模式匹配集合；Step S22: performing subsequence pattern matching on the financial transaction behavior information block according to the time series feature sequence to generate a behavior pattern matching set;
本实施例中，通过采用动态时间规整（DTW）算法对提取的时序特征序列进行子序列模式匹配，将时序特征序列分割成多个子序列，并对这些子序列与已知的交易模式进行比对，通过DTW算法计算子序列与模板模式之间的相似度，从而识别出匹配的交易行为模式，将所有匹配的结果汇总为行为模式匹配集合，该集合包含了识别出的交易模式及其对应的匹配程度，为后续的模式分析提供数据支持。In this embodiment, the dynamic time warping (DTW) algorithm is used to perform subsequence pattern matching on the extracted time series feature sequence, the time series feature sequence is divided into multiple subsequences, and these subsequences are compared with known transaction patterns. The similarity between the subsequences and the template pattern is calculated by the DTW algorithm to identify the matching transaction behavior pattern, and all matching results are summarized into a behavior pattern matching set, which includes the identified transaction patterns and their corresponding matching degrees, providing data support for subsequent pattern analysis.
步骤S23：对行为模式匹配集合进行中心点聚类处理，得到模式中心数据；Step S23: performing center point clustering processing on the behavior pattern matching set to obtain pattern center data;
本实施例中，应用K均值聚类算法对行为模式匹配集合中的数据进行聚类分析，根据匹配结果中的行为模式特征向量，使用K均值算法设定聚类中心数量K，并对数据进行迭代处理，算法通过计算各数据点到每个聚类中心的距离，将数据点归类到距离最近的中心点所在的簇中，最终，通过计算每个簇的中心点坐标，生成模式中心数据。In this embodiment, the K-means clustering algorithm is applied to perform cluster analysis on the data in the behavior pattern matching set. According to the behavior pattern feature vector in the matching result, the K-means algorithm is used to set the number of cluster centers K, and the data is iteratively processed. The algorithm calculates the distance from each data point to each cluster center, and classifies the data points into the cluster with the nearest center point. Finally, the pattern center data is generated by calculating the coordinates of the center point of each cluster.
步骤S24：对模式中心数据进行变化趋势分析，生成模式趋势数据；Step S24: Analyze the change trend of the pattern center data to generate pattern trend data;
本实施例中，对生成的模式中心数据进行变化趋势分析，采用时间序列分析方法，例如自回归移动平均（ARMA）模型，对模式中心数据的时间变化趋势进行建模，通过对模式中心数据进行平滑处理和趋势分解，识别出长期趋势、周期性变化以及随机波动，将这些分析结果整合，生成模式趋势数据，展示不同金融行为模式随时间的变化趋势，帮助识别潜在的趋势变动。In this embodiment, the generated pattern center data is subjected to a change trend analysis, and a time series analysis method, such as an autoregressive moving average (ARMA) model, is used to model the time change trend of the pattern center data. By smoothing and trend decomposing the pattern center data, long-term trends, cyclical changes, and random fluctuations are identified. These analysis results are integrated to generate pattern trend data, which shows the change trends of different financial behavior patterns over time and helps identify potential trend changes.
步骤S25：基于模式趋势数据对金融交易行为信息块进行行为趋势预测，生成预测行为数据。Step S25: Predicting the behavior trend of the financial transaction behavior information block based on the pattern trend data to generate predicted behavior data.
本实施例中，应用预测模型（如递归神经网络中的长短期记忆网络LSTM）对模式趋势数据进行分析，将模式趋势数据输入到LSTM模型中，训练模型以识别数据中的时间序列特征和趋势。通过模型的训练过程，生成未来的行为趋势预测结果，未来的行为趋势预测结果包括未来会出现的金融交易行为模式的变化趋势、交易量预期等，最终生成预测行为数据。In this embodiment, a prediction model (such as a long short-term memory network LSTM in a recursive neural network) is applied to analyze the pattern trend data, the pattern trend data is input into the LSTM model, and the model is trained to identify the time series features and trends in the data. Through the training process of the model, future behavior trend prediction results are generated, and the future behavior trend prediction results include the changing trend of the financial transaction behavior pattern that will appear in the future, the expected transaction volume, etc., and finally the predicted behavior data is generated.
优选地，步骤S3包括：Preferably, step S3 comprises:
步骤S31：对预测行为数据进行持续同调分析，得到行为拓扑特征谱；Step S31: performing continuous coherence analysis on the predicted behavior data to obtain a behavior topology feature spectrum;
步骤S32：根据行为拓扑特征谱对用户基础信息块进行多层异质网络构建，得到用户-行为关联网络；Step S32: construct a multi-layer heterogeneous network for the user basic information block according to the behavior topology feature spectrum to obtain a user-behavior association network;
步骤S33：对用户-行为关联网络进行邻接矩阵解析，生成共识信息矩阵；Step S33: performing adjacency matrix analysis on the user-behavior association network to generate a consensus information matrix;
步骤S34：对共识信息矩阵进行分层融合处理，得到用户行为共识数据。Step S34: Perform hierarchical fusion processing on the consensus information matrix to obtain user behavior consensus data.
!通过对预测行为数据进行持续同调分析，可以得到行为拓扑特征谱，能够揭示行为数据的复杂结构和模式，有助于更准确地理解用户的行为特征和动态变化。基于行为拓扑特征谱对用户基础信息块进行多层异质网络构建，能够生成用户-行为关联网络，能够整合不同层次的用户信息和行为数据，为综合分析提供全面的视角。对用户-行为关联网络进行邻接矩阵解析，可以生成共识信息矩阵，有助于揭示用户行为之间的关联性和共识程度，从而提高对用户行为模式的理解和识别能力。对共识信息矩阵进行分层融合处理，能够得到用户行为共识数据，有助于综合不同层次的信息，从而生成更为准确和可靠的用户行为共识数据。行为拓扑特征谱和用户-行为关联网络的构建，结合共识信息矩阵和共识数据，能够提供深入的用户行为洞察力，能够帮助识别用户的潜在行为模式和异常行为，提升安全防护的精准度。通过分析用户行为的共识数据，可以更有效地检测和预警异常行为，例如，通过发现用户行为与共识数据的不一致性，可以及早发现欺诈行为或安全风险。多层异质网络构建和分层融合处理的技术特征能够整合来自不同来源的数据，提升数据的综合利用价值，有助于实现对金融信息的全面分析和处理。综合分析用户行为共识数据和网络结构，有助于优化风险管理策略，提升金融信息系统的安全防护能力。!By continuously coherently analyzing the predicted behavior data, we can obtain the behavior topology feature spectrum, which can reveal the complex structure and pattern of the behavior data, and help to more accurately understand the user's behavior characteristics and dynamic changes. Based on the behavior topology feature spectrum, the user basic information block is constructed with multiple layers of heterogeneous networks, which can generate a user-behavior association network, integrate user information and behavior data at different levels, and provide a comprehensive perspective for comprehensive analysis. Adjacency matrix analysis of the user-behavior association network can generate a consensus information matrix, which helps to reveal the correlation and consensus between user behaviors, thereby improving the understanding and recognition of user behavior patterns. Layered fusion processing of the consensus information matrix can obtain user behavior consensus data, which helps to integrate information at different levels, thereby generating more accurate and reliable user behavior consensus data. The construction of the behavior topology feature spectrum and the user-behavior association network, combined with the consensus information matrix and consensus data, can provide in-depth insights into user behavior, help identify users' potential behavior patterns and abnormal behaviors, and improve the accuracy of security protection. By analyzing the consensus data of user behavior, abnormal behavior can be detected and warned more effectively. For example, by discovering the inconsistency between user behavior and consensus data, fraud or security risks can be discovered early. The technical features of multi-layer heterogeneous network construction and hierarchical fusion processing can integrate data from different sources, enhance the comprehensive utilization value of data, and help to achieve comprehensive analysis and processing of financial information. Comprehensive analysis of user behavior consensus data and network structure can help optimize risk management strategies and enhance the security protection capabilities of financial information systems.
作为本发明的一个实例，参考图3所示，在本实例中所述步骤S3包括：As an example of the present invention, referring to FIG3 , in this example, step S3 includes:
步骤S31：对预测行为数据进行持续同调分析，得到行为拓扑特征谱；Step S31: performing continuous coherence analysis on the predicted behavior data to obtain a behavior topology feature spectrum;
本实施例中，通过对预测行为数据中的时间序列进行数据预处理，使用标准化方法将数据范围缩放至统一区间，应用持久同调技术对处理后的数据进行分析，持久同调技术采用生成的简化形状（如点集、边界和孔洞）来捕捉数据的拓扑特征，使用持久同调算法计算数据的Betti数，得到每个拓扑特征的持久性，从而生成行为拓扑特征谱，该特征谱表示了数据中各个拓扑结构的持久性，能够揭示预测行为数据中的长期结构和变化模式。In this embodiment, data preprocessing is performed on the time series in the predicted behavior data, the data range is scaled to a uniform interval using a standardized method, and the processed data is analyzed using a persistent homology technique. The persistent homology technique uses generated simplified shapes (such as point sets, boundaries, and holes) to capture the topological features of the data. The Betti number of the data is calculated using a persistent homology algorithm to obtain the persistence of each topological feature, thereby generating a behavioral topological feature spectrum. The feature spectrum represents the persistence of each topological structure in the data and can reveal the long-term structure and change pattern in the predicted behavior data.
步骤S32：根据行为拓扑特征谱对用户基础信息块进行多层异质网络构建，得到用户-行为关联网络；Step S32: construct a multi-layer heterogeneous network for the user basic information block according to the behavior topology feature spectrum to obtain a user-behavior association network;
本实施例中，基于行为拓扑特征谱，将用户基础信息块与行为特征进行关联，定义多层异质网络的节点类型，包括用户节点、行为节点和拓扑特征节点，使用行为拓扑特征谱中的信息建立用户与行为之间的关联边，例如，通过设定权重值反映用户参与某种行为的强度，构建多层异质网络，其中每一层表示一种关系类型（如用户行为关联、行为拓扑特征关联等），形成用户-行为关联网络，该网络综合了用户信息、行为特征和拓扑特征的多维度关联数据。In this embodiment, based on the behavioral topological feature spectrum, the user basic information block is associated with the behavioral features, and the node types of the multi-layer heterogeneous network are defined, including user nodes, behavior nodes, and topological feature nodes. The information in the behavioral topological feature spectrum is used to establish the association edge between the user and the behavior. For example, by setting the weight value to reflect the intensity of the user's participation in a certain behavior, a multi-layer heterogeneous network is constructed, in which each layer represents a relationship type (such as user behavior association, behavioral topological feature association, etc.), forming a user-behavior association network, which integrates multi-dimensional association data of user information, behavioral characteristics, and topological characteristics.
步骤S33：对用户-行为关联网络进行邻接矩阵解析，生成共识信息矩阵；Step S33: performing adjacency matrix analysis on the user-behavior association network to generate a consensus information matrix;
本实施例中，将用户-行为关联网络转化为邻接矩阵形式，其中矩阵中的每一个元素表示网络中节点之间的连接关系及其强度，采用图谱分析方法计算邻接矩阵中的连接权重，并对矩阵进行规范化处理，以消除不同维度数据的影响，通过计算矩阵的特征值分解或谱聚类，提取出网络中关键节点的共识信息，生成的共识信息矩阵展示了用户与行为之间的整体关联强度，并反映了用户行为的集体特征和趋势。In this embodiment, the user-behavior association network is converted into an adjacency matrix form, where each element in the matrix represents the connection relationship and its strength between nodes in the network. The connection weights in the adjacency matrix are calculated using a graph analysis method, and the matrix is normalized to eliminate the influence of data of different dimensions. The consensus information of key nodes in the network is extracted by calculating the eigenvalue decomposition or spectral clustering of the matrix. The generated consensus information matrix shows the overall association strength between users and behaviors, and reflects the collective characteristics and trends of user behaviors.
步骤S34：对共识信息矩阵进行分层融合处理，得到用户行为共识数据。Step S34: Perform hierarchical fusion processing on the consensus information matrix to obtain user behavior consensus data.
本实施例中，对共识信息矩阵进行分层处理，根据矩阵的不同部分（如用户与行为的基本关联、深层次的行为模式等）进行层次划分，采用加权融合方法对每一层的共识信息进行融合，结合不同层次的信息来构建综合的数据视图，例如，通过加权平均或多层次融合模型，将层次化信息汇总到一个统一的用户行为共识数据集中，生成的用户行为共识数据涵盖了用户行为的全局和局部特征。In this embodiment, the consensus information matrix is processed in layers, and the layers are divided according to different parts of the matrix (such as the basic association between users and behaviors, deep behavioral patterns, etc.). The consensus information of each layer is fused using a weighted fusion method, and information at different levels is combined to construct a comprehensive data view. For example, through weighted averaging or a multi-level fusion model, the hierarchical information is aggregated into a unified user behavior consensus data set. The generated user behavior consensus data covers the global and local characteristics of user behavior.
优选地，步骤S4包括：Preferably, step S4 comprises:
步骤S41：对用户行为共识数据进行用户唯一标识构建，得到用户标识指纹；Step S41: construct a unique user identifier for the user behavior consensus data to obtain a user identifier fingerprint;
步骤S42：基于用户标识指纹对用户行为共识数据进行对应唯一行为识别，生成对应唯一行为数据；Step S42: performing corresponding unique behavior identification on the user behavior consensus data based on the user identification fingerprint to generate corresponding unique behavior data;
步骤S43：对用户标识指纹及对应唯一行为数据进行联合特征融合，得到动态加密参数；Step S43: performing joint feature fusion on the user identification fingerprint and the corresponding unique behavior data to obtain dynamic encryption parameters;
步骤S44：基于动态加密参数对多模态金融信息进行动态联合加密，生成加密金融信息。Step S44: Dynamically jointly encrypt the multimodal financial information based on the dynamic encryption parameters to generate encrypted financial information.
本发明通过对用户行为共识数据进行用户唯一标识构建，生成用户标识指纹，能够唯一标识每个用户，提高了对用户身份的认证和验证能力，从而增强了信息安全性。基于用户标识指纹对用户行为共识数据进行对应唯一行为识别，生成对应唯一行为数据，可以准确区分不同用户的行为模式，有助于识别用户的特定行为特征和异常活动。通过对用户标识指纹及对应唯一行为数据进行联合特征融合，得到动态加密参数，结合了用户的行为特征和身份信息，增强了加密过程的安全性和适应性。基于动态加密参数对多模态金融信息进行动态联合加密，生成加密金融信息，能够确保金融信息在存储和传输过程中保持高水平的安全性，防止未经授权的访问和数据泄露。通过生成用户唯一标识指纹和动态加密参数，能够显著提升数据的安全性，防止数据被恶意篡改或非法访问，提升的数据安全性对于保护敏感金融信息至关重要。动态加密参数的使用使得加密过程具有更高的复杂性和灵活性，从而防止伪造和欺诈行为，使得加密信息难以被破解，增强了对金融交易的保护。通过用户标识指纹和行为数据的结合，能够提供针对每个用户的个性化安全防护，使得系统能够更精准地识别和应对用户特定的安全需求。联合特征融合和动态加密过程优化了金融信息的处理和保护流程，提高了数据加密的效率和有效性，通过自动化和智能化的处理，减少了人工干预和处理错误的发生。The present invention can uniquely identify each user by constructing a user unique identification for user behavior consensus data and generating a user identification fingerprint, thereby improving the authentication and verification capabilities of user identities and enhancing information security. Based on the user identification fingerprint, the user behavior consensus data is identified with corresponding unique behaviors and corresponding unique behavior data is generated, which can accurately distinguish the behavior patterns of different users and help identify the specific behavior characteristics and abnormal activities of users. By performing joint feature fusion on the user identification fingerprint and the corresponding unique behavior data, dynamic encryption parameters are obtained, which combine the user's behavior characteristics and identity information, and enhance the security and adaptability of the encryption process. Based on the dynamic encryption parameters, multimodal financial information is dynamically jointly encrypted to generate encrypted financial information, which can ensure that the financial information maintains a high level of security during storage and transmission, and prevent unauthorized access and data leakage. By generating a user unique identification fingerprint and dynamic encryption parameters, the security of the data can be significantly improved, and the data can be prevented from being maliciously tampered with or illegally accessed. The improved data security is crucial for protecting sensitive financial information. The use of dynamic encryption parameters makes the encryption process more complex and flexible, thereby preventing forgery and fraud, making it difficult to crack encrypted information, and enhancing the protection of financial transactions. By combining user identification fingerprints and behavioral data, it is possible to provide personalized security protection for each user, allowing the system to more accurately identify and respond to user-specific security needs. The joint feature fusion and dynamic encryption process optimizes the processing and protection of financial information, improves the efficiency and effectiveness of data encryption, and reduces manual intervention and processing errors through automated and intelligent processing.
本实施例中，使用加密哈希算法（例如SHA-256）对用户行为共识数据进行处理，具体操作包括从用户行为共识数据中提取用户的所有活动记录，例如登录时间、交易历史、行为模式等，将这些信息进行拼接，形成一个数据串。通过应用哈希函数，对数据串进行加密处理，生成固定长度的哈希值，该哈希值即为用户的唯一标识指纹，能够唯一标识每个用户并确保数据的一致性和隐私保护。基于用户标识指纹，通过将该指纹作为输入，利用机器学习分类模型（如随机森林或支持向量机）进行行为识别。模型训练过程使用标注过的用户行为数据进行训练，生成用户行为模式的分类器，输入用户标识指纹后，分类器会识别该用户的行为模式，并将识别结果与用户的行为记录进行匹配，生成对应唯一行为数据。此数据具体反映了每个用户的行为特征，如频繁操作的功能、交易习惯。对用户标识指纹及对应唯一行为数据进行特征提取，将用户标识指纹和对应唯一行为数据转化为数值特征向量，特征提取过程中，使用特征工程技术，例如标准化、归一化处理，采用特征融合算法（如主成分分析PCA或特征级联）将这两个特征向量进行合并，生成一个联合特征向量，该向量经过进一步的处理，例如加权处理或通过加密算法（如AES）生成最终的动态加密参数。通过动态加密参数作为密钥，通过对称加密算法（如AES或RSA）对多模态金融信息进行加密，具体操作包括将多模态金融信息（如交易记录、用户个人信息等）分割成适合的块，并对每个块应用加密算法，加密过程中，将动态加密参数用于生成每个数据块的加密密钥，数据块经过加密后形成加密金融信息，该信息在存储和传输过程中能够有效保护数据隐私，确保只有拥有解密密钥的授权用户可以访问原始金融信息。In this embodiment, the user behavior consensus data is processed using a cryptographic hash algorithm (such as SHA-256). The specific operation includes extracting all the user's activity records from the user behavior consensus data, such as login time, transaction history, behavior pattern, etc., and splicing this information to form a data string. By applying the hash function, the data string is encrypted to generate a hash value of a fixed length. The hash value is the user's unique identification fingerprint, which can uniquely identify each user and ensure data consistency and privacy protection. Based on the user identification fingerprint, the fingerprint is used as input to use a machine learning classification model (such as a random forest or a support vector machine) for behavior recognition. The model training process uses the labeled user behavior data for training to generate a classifier of the user behavior pattern. After the user identification fingerprint is input, the classifier will identify the user's behavior pattern and match the identification result with the user's behavior record to generate the corresponding unique behavior data. This data specifically reflects the behavioral characteristics of each user, such as frequently operated functions and transaction habits. The user identification fingerprint and the corresponding unique behavior data are extracted and converted into numerical feature vectors. During the feature extraction process, feature engineering techniques are used, such as standardization and normalization. The two feature vectors are merged by a feature fusion algorithm (such as principal component analysis PCA or feature concatenation) to generate a joint feature vector. The vector is further processed, such as weighted processing or the final dynamic encryption parameter is generated by an encryption algorithm (such as AES). The multimodal financial information is encrypted by a symmetric encryption algorithm (such as AES or RSA) using the dynamic encryption parameter as a key. The specific operation includes dividing the multimodal financial information (such as transaction records, user personal information, etc.) into suitable blocks and applying the encryption algorithm to each block. During the encryption process, the dynamic encryption parameter is used to generate the encryption key for each data block. After the data block is encrypted, it forms encrypted financial information. This information can effectively protect data privacy during storage and transmission, ensuring that only authorized users with decryption keys can access the original financial information.
优选地，对用户标识指纹及对应唯一行为数据进行联合特征融合包括：Preferably, the joint feature fusion of the user identification fingerprint and the corresponding unique behavior data includes:
对用户标识指纹及对应唯一行为数据进行特征分层处理，得到多层次特征图谱；Perform feature layering processing on user identification fingerprints and corresponding unique behavior data to obtain a multi-level feature map;
根据多层次特征图谱对用户标识指纹及对应唯一行为数据进行关联特征映射，得到层次关联特征；According to the multi-level feature map, the user identification fingerprint and the corresponding unique behavior data are mapped to obtain hierarchical correlation features;
基于层次关联特征对用户标识指纹及对应唯一行为数据进行联合特征融合，得到动态加密参数。Based on the hierarchical correlation features, the user identification fingerprint and the corresponding unique behavior data are jointly fused to obtain dynamic encryption parameters.
本发明通过对用户标识指纹及对应唯一行为数据进行特征分层处理，得到多层次特征图谱，能够全面地提取和表示数据的不同层次信息，从而提供对用户行为和身份的更深入理解。基于多层次特征图谱进行关联特征映射，生成层次关联特征，能够揭示用户标识指纹与行为数据之间的复杂关系，提升了对用户特征的准确识别和分析能力。基于层次关联特征进行联合特征融合，生成动态加密参数，能够综合考虑用户的多重特征，生成具有高复杂性和适应性的加密参数，提高了加密的安全性。通过联合特征融合生成的动态加密参数具有较高的复杂性和变动性，增强了加密强度，防止简单的破解和攻击，通过动态特性使得加密过程更具安全性，保护金融数据不被未授权访问。多层次特征图谱和层次关联特征的使用提高了数据保护的精准度，通过综合用户标识和行为数据特征，生成的动态加密参数能够针对不同用户的行为模式提供定制化保护。动态加密参数的生成使得安全策略可以根据用户行为和身份特征的变化进行调整，能够在不同情况下应用适应性的安全策略，增强对金融信息的保护能力。通过特征融合生成的动态加密参数是独特且难以预测的，显著减少了伪造和欺诈行为的风险，确保了每次加密操作的唯一性和安全性。结合用户标识指纹和行为数据的特征融合方法，使得金融信息安全防护系统能够智能化地应对各种潜在威胁，提高整体安全防护的效率和效果。The present invention obtains a multi-level feature map by performing feature hierarchical processing on the user identification fingerprint and the corresponding unique behavior data, which can comprehensively extract and represent different levels of data information, thereby providing a deeper understanding of user behavior and identity. Based on the multi-level feature map, the associated feature mapping is performed to generate hierarchical associated features, which can reveal the complex relationship between the user identification fingerprint and the behavior data, and improve the accurate identification and analysis capabilities of user features. Based on the hierarchical associated features, joint feature fusion is performed to generate dynamic encryption parameters, which can comprehensively consider the multiple features of the user, generate encryption parameters with high complexity and adaptability, and improve the security of encryption. The dynamic encryption parameters generated by the joint feature fusion have high complexity and variability, enhance the encryption strength, prevent simple cracking and attacks, make the encryption process more secure through dynamic characteristics, and protect financial data from unauthorized access. The use of multi-level feature maps and hierarchical associated features improves the accuracy of data protection. By integrating the user identification and behavior data features, the generated dynamic encryption parameters can provide customized protection for the behavior patterns of different users. The generation of dynamic encryption parameters enables security policies to be adjusted according to changes in user behavior and identity characteristics, and can apply adaptive security policies in different situations to enhance the protection capabilities of financial information. The dynamic encryption parameters generated by feature fusion are unique and difficult to predict, significantly reducing the risk of forgery and fraud, and ensuring the uniqueness and security of each encryption operation. The feature fusion method combining user identification fingerprints and behavioral data enables the financial information security protection system to intelligently respond to various potential threats and improve the efficiency and effectiveness of overall security protection.
本实施例中，将用户标识指纹和对应唯一行为数据转化为数值型特征向量，进行初步数据预处理，包括去噪声和标准化。对每个特征向量进行层次化处理，使用特征分层算法如自编码器，将特征向量分解为多个层次，这些层次包括原始特征层、次级特征层和高级特征层，构建一个自编码器网络，输入用户标识指纹和行为数据特征，通过多个隐藏层逐步抽取特征，形成多层次特征图谱，该图谱展示了不同层次的特征表示，反映了数据的不同抽象层级和关联程度。根据多层次特征图谱对用户标识指纹及对应唯一行为数据进行关联特征映射，得到层次关联特征。利用多层次特征图谱中的各层特征，通过特征映射算法（如相似度计算或卷积操作）将这些特征进行关联映射，具体包括构建一个关联映射矩阵，其中每个元素表示两个特征之间的相似度或相关性，利用相似度计算（例如余弦相似度）对不同层次的特征进行匹配，将用户标识指纹和行为数据中的特征按照关联程度进行映射，生成的层次关联特征矩阵反映了不同特征在层次结构中的关联关系，展示了数据特征间的复杂交互。将层次关联特征矩阵进行处理，将其作为联合特征融合的输入，使用融合算法（如加权求和或融合网络）对不同层次的特征进行组合，构建一个融合网络，该网络采用加权平均机制，将层次关联特征按照预设的权重进行融合，权重根据特征的重要性进行动态调整，通过该网络结合用户标识指纹的特征和行为数据的特征，生成一个统一的特征向量，作为动态加密参数。In this embodiment, the user identification fingerprint and the corresponding unique behavior data are converted into numerical feature vectors, and preliminary data preprocessing is performed, including denoising and standardization. Each feature vector is hierarchically processed, and a feature hierarchical algorithm such as an autoencoder is used to decompose the feature vector into multiple levels, including the original feature layer, the secondary feature layer, and the high-level feature layer. An autoencoder network is constructed, and the user identification fingerprint and behavior data features are input. Features are gradually extracted through multiple hidden layers to form a multi-level feature map, which shows feature representations at different levels, reflecting different abstract levels and correlation levels of the data. According to the multi-level feature map, the user identification fingerprint and the corresponding unique behavior data are associated with feature mapping to obtain hierarchical association features. Using the features of each layer in the multi-level feature map, these features are associated and mapped through feature mapping algorithms (such as similarity calculation or convolution operation), specifically including constructing an association mapping matrix, in which each element represents the similarity or correlation between two features, using similarity calculation (such as cosine similarity) to match features at different levels, mapping the features in the user identification fingerprint and behavior data according to the degree of association, and the generated hierarchical association feature matrix reflects the association relationship between different features in the hierarchy and shows the complex interaction between data features. The hierarchical association feature matrix is processed and used as the input of joint feature fusion. The features of different levels are combined using a fusion algorithm (such as weighted summation or fusion network) to construct a fusion network. The network adopts a weighted average mechanism to fuse the hierarchical association features according to the preset weights. The weights are dynamically adjusted according to the importance of the features. The network combines the features of the user identification fingerprint and the features of the behavior data to generate a unified feature vector as a dynamic encryption parameter.
优选地，步骤S5包括：Preferably, step S5 comprises:
步骤S51：对预测行为数据进行攻击策略网络构建，生成预测攻击策略网络；Step S51: constructing an attack strategy network for the predicted behavior data to generate a predicted attack strategy network;
步骤S52：根据预测攻击策略网络对加密金融信息进行对抗样本演化，得到模拟攻击数据；Step S52: Evolving adversarial samples on encrypted financial information according to the predicted attack strategy network to obtain simulated attack data;
步骤S53：基于模拟攻击数据对加密金融信息进行漏洞攻击模拟，生成模拟攻击金融信息；Step S53: simulating a vulnerability attack on the encrypted financial information based on the simulated attack data to generate simulated attack financial information;
步骤S54：对模拟攻击金融信息进行安全威胁筛查，得到安全威胁数据。Step S54: Perform security threat screening on the simulated attack financial information to obtain security threat data.
本发明通过对预测行为数据进行攻击策略网络构建，生成预测攻击策略网络，能够模拟和预测潜在的攻击策略，提供对金融信息系统面临的各种攻击手段的全面分析，提升对攻击模式的识别能力。根据攻击策略网络对加密金融信息进行对抗样本演化，得到模拟攻击数据，可以帮助模拟攻击情景，从而测试和强化加密金融信息的抗攻击能力。基于模拟攻击数据对加密金融信息进行漏洞攻击模拟，生成模拟攻击金融信息，能够识别加密金融信息中的潜在漏洞，帮助评估系统的安全防护效果，并揭示系统存在的安全缺陷。对模拟攻击金融信息进行安全威胁筛查，得到安全威胁数据，能够准确检测和评估金融信息系统中的安全威胁，提供针对性的安全防护建议和改进措施。通过构建攻击策略网络和模拟攻击，能够提前识别和预测潜在的安全威胁，有助于在真实攻击发生之前采取防护措施，从而提升系统的安全性。对加密金融信息进行对抗样本演化和漏洞攻击模拟，可以强化加密系统的防御能力。通过模拟攻击和测试，确保加密技术能够抵御各种攻击，提升系统的安全性和可靠性。通过分析安全威胁数据，可以优化现有的安全防护策略，制定更加有效的应对措施，有助于在实际攻击中更好地保护金融信息，减少潜在的安全风险。漏洞攻击模拟能够帮助发现和修复系统中的安全漏洞，减少漏洞带来的安全隐患，能够在系统投入使用前确保其安全性。通过综合分析攻击策略和模拟攻击结果，可以提高风险管理的能力和水平，有助于制定全面的安全策略，保障金融信息的安全性和完整性。The present invention constructs an attack strategy network for predicted behavior data to generate a predicted attack strategy network, which can simulate and predict potential attack strategies, provide a comprehensive analysis of various attack methods faced by financial information systems, and improve the ability to identify attack patterns. According to the attack strategy network, encrypted financial information is subjected to adversarial sample evolution to obtain simulated attack data, which can help simulate attack scenarios, thereby testing and strengthening the anti-attack ability of encrypted financial information. Based on the simulated attack data, vulnerability attack simulation is performed on encrypted financial information to generate simulated attack financial information, which can identify potential vulnerabilities in encrypted financial information, help evaluate the security protection effect of the system, and reveal the security defects of the system. Security threat screening is performed on simulated attack financial information to obtain security threat data, which can accurately detect and evaluate security threats in financial information systems, and provide targeted security protection suggestions and improvement measures. By constructing an attack strategy network and simulating attacks, potential security threats can be identified and predicted in advance, which helps to take protective measures before real attacks occur, thereby improving the security of the system. Evolution of adversarial samples and simulation of vulnerability attacks on encrypted financial information can strengthen the defense capability of the encryption system. Through simulated attacks and tests, it is ensured that encryption technology can resist various attacks and improve the security and reliability of the system. By analyzing security threat data, we can optimize existing security protection strategies and develop more effective countermeasures, which will help better protect financial information in actual attacks and reduce potential security risks. Vulnerability attack simulation can help discover and repair security vulnerabilities in the system, reduce the security risks caused by vulnerabilities, and ensure the security of the system before it is put into use. By comprehensively analyzing attack strategies and simulating attack results, we can improve the ability and level of risk management, help develop comprehensive security strategies, and ensure the security and integrity of financial information.
本实施例中，对收集到的预测行为数据进行特征提取，通过特征工程方法，如主成分分析（PCA）或特征选择算法，将数据中的重要特征提取出来，利用图结构学习算法，例如基于博弈论的网络生成方法或生成对抗网络（GAN），构建攻击策略网络，在网络构建过程中，每个节点代表一种潜在攻击策略，节点之间的连接表示不同策略之间的关联或依赖关系，通过迭代优化算法对网络进行调整和优化，使得生成的攻击策略网络能够有效模拟真实环境中的潜在攻击路径和策略组合，最终得到预测攻击策略网络。在攻击策略网络的基础上，利用进化算法或遗传算法对加密金融信息进行对抗样本生成，具体操作为，初始化一组对抗样本，这些样本通过扰动原始加密金融信息中的特定特征而生成，在进化算法的驱动下，样本群体经历多个迭代，每次迭代中，基于攻击策略网络对样本进行适应度评估和选择，保留攻击效果更强的样本，并通过交叉、变异操作产生新一代对抗样本，该过程持续进行，直到演化出的对抗样本能够成功攻击原加密金融信息的安全机制，生成的模拟攻击数据用于模拟真实攻击场景下出现的攻击行为。在获得的模拟攻击数据基础上，采用渗透测试工具或自定义漏洞扫描器对加密金融信息进行漏洞攻击模拟，将模拟攻击数据输入漏洞扫描器中，扫描器基于预设的漏洞规则库和模拟攻击策略对加密金融信息进行深度分析，识别潜在的安全漏洞，在分析过程中，利用静态分析技术和动态分析技术相结合的方式，分别对加密算法、密钥管理机制、以及数据传输路径进行全面检查，漏洞攻击模拟结果经过分析后，生成的模拟攻击金融信息详细描述了在模拟攻击场景下，加密金融信息所暴露出的漏洞以及安全隐患。利用预训练的机器学习模型对模拟攻击金融信息进行分类和标注，识别不同类型的安全威胁，应用基于规则的安全审计系统，对识别出的威胁进行进一步验证和筛查，审计系统通过匹配已有的威胁库，检测模拟攻击金融信息中是否存在已知或未知的威胁模式，对于未知威胁，采用基于异常检测的算法，如孤立森林或自动编码器，进行行为异常分析，通过结合机器学习模型输出的威胁标签和安全审计系统的分析结果，筛查出最具威胁性的安全隐患，形成安全威胁数据，确保对潜在安全问题的全面掌握。In this embodiment, feature extraction is performed on the collected prediction behavior data. The important features in the data are extracted through feature engineering methods, such as principal component analysis (PCA) or feature selection algorithms. A graph structure learning algorithm, such as a network generation method based on game theory or a generative adversarial network (GAN), is used to construct an attack strategy network. In the network construction process, each node represents a potential attack strategy, and the connection between nodes represents the association or dependency relationship between different strategies. The network is adjusted and optimized through an iterative optimization algorithm, so that the generated attack strategy network can effectively simulate the potential attack paths and strategy combinations in the real environment, and finally a predicted attack strategy network is obtained. Based on the attack strategy network, evolutionary algorithms or genetic algorithms are used to generate adversarial samples for encrypted financial information. The specific operation is to initialize a group of adversarial samples. These samples are generated by perturbing specific features in the original encrypted financial information. Driven by the evolutionary algorithm, the sample group undergoes multiple iterations. In each iteration, the fitness of the samples is evaluated and selected based on the attack strategy network, and samples with stronger attack effects are retained. A new generation of adversarial samples is generated through crossover and mutation operations. This process continues until the evolved adversarial samples can successfully attack the security mechanism of the original encrypted financial information. The generated simulated attack data is used to simulate the attack behavior that occurs in the real attack scenario. Based on the obtained simulated attack data, a penetration testing tool or a custom vulnerability scanner is used to simulate vulnerability attacks on encrypted financial information. The simulated attack data is input into the vulnerability scanner. The scanner conducts in-depth analysis of the encrypted financial information based on the preset vulnerability rule library and simulated attack strategy to identify potential security vulnerabilities. In the analysis process, the encryption algorithm, key management mechanism, and data transmission path are comprehensively checked by combining static analysis technology and dynamic analysis technology. After the vulnerability attack simulation results are analyzed, the generated simulated attack financial information describes in detail the vulnerabilities and security risks exposed by the encrypted financial information in the simulated attack scenario. The simulated attack financial information is classified and labeled using a pre-trained machine learning model to identify different types of security threats. A rule-based security audit system is applied to further verify and screen the identified threats. The audit system detects whether there are known or unknown threat patterns in the simulated attack financial information by matching the existing threat library. For unknown threats, anomaly detection-based algorithms, such as isolation forests or autoencoders, are used to perform behavioral anomaly analysis. By combining the threat labels output by the machine learning model and the analysis results of the security audit system, the most threatening security risks are screened out to form security threat data to ensure a comprehensive grasp of potential security issues.
优选地，对模拟攻击金融信息进行安全威胁筛查包括：Preferably, the security threat screening of simulated attack financial information includes:
对模拟攻击金融信息进行时序转换，得到模拟时序数据；Perform time series conversion on the simulated attack financial information to obtain simulated time series data;
对模拟时序数据进行变化趋势分析，生成防护变化趋势；Conduct change trend analysis on simulated time series data to generate protection change trends;
基于防护变化趋势对模拟时序数据进行变化数据定位，得到安全数据变化点；Based on the protection change trend, the simulation time series data is changed to locate the data change point and obtain the safety data change point;
根据安全数据变化点对模拟攻击金融信息进行安全威胁筛查，得到安全威胁数据。Security threat screening is performed on the simulated attack financial information according to the security data change points to obtain security threat data.
本发明通过对模拟攻击金融信息进行时序转换，得到模拟时序数据，可以从时间维度精确地捕捉信息变化过程，为后续的趋势分析奠定基础。对模拟时序数据进行变化趋势分析，生成防护变化趋势，能够识别出攻击过程中安全防护的变化趋势，深入理解攻击行为如何影响金融信息的安全性，有助于及时调整防护策略。基于防护变化趋势，对模拟时序数据进行变化数据定位，得到安全数据变化点，能够精确定位到信息安全的关键变化点，有助于快速识别潜在的安全威胁区域，减少分析的时间和资源消耗。根据安全数据变化点，对模拟攻击金融信息进行安全威胁筛查，得到安全威胁数据，能精确识别出攻击行为中的安全漏洞或弱点，为防御系统的改进提供具体的数据支持，提升筛查的精准度。通过时序转换和趋势分析，能够实现对金融信息系统的实时安全监测，有助于在安全威胁刚刚出现时就进行有效干预，防止威胁的扩大和潜在的损失。变化趋势分析可以提供前瞻性的威胁预测能力，通过分析趋势的变化，预见未来发生的安全事件，并采取预防措施，有助于提升系统的整体安全性。通过对变化数据的精准定位，安全防护措施可以针对具体的安全数据变化点进行调整和强化，实现精细化的安全防护，减少了不必要的资源浪费，提高了防护效率。通过时序数据的分析和变化点的识别，安全防护系统能够动态应对不断变化的安全威胁，确保系统能够适应不同攻击场景的变化，保持高效的防护能力。精确的变化数据定位和威胁筛查减少了误报率，避免不必要的安全警报，提升了安全防护系统的有效性和可信度。The present invention can accurately capture the information change process from the time dimension by performing time series conversion on the simulated attack financial information to obtain simulated time series data, laying the foundation for subsequent trend analysis. The simulated time series data is analyzed for change trends to generate protection change trends, which can identify the change trends of security protection during the attack process, deeply understand how the attack behavior affects the security of financial information, and help to adjust the protection strategy in time. Based on the protection change trend, the simulated time series data is located for change data to obtain the security data change point, which can accurately locate the key change point of information security, help to quickly identify potential security threat areas, and reduce the time and resource consumption of analysis. According to the security data change point, the simulated attack financial information is screened for security threats to obtain security threat data, which can accurately identify security loopholes or weaknesses in the attack behavior, provide specific data support for the improvement of the defense system, and improve the accuracy of screening. Through time series conversion and trend analysis, real-time security monitoring of the financial information system can be achieved, which helps to effectively intervene when the security threat just appears to prevent the expansion of the threat and potential losses. Change trend analysis can provide forward-looking threat prediction capabilities. By analyzing the changes in trends, future security incidents can be foreseen and preventive measures can be taken, which helps to improve the overall security of the system. By accurately locating the changing data, security protection measures can be adjusted and strengthened according to the specific security data change points, achieving refined security protection, reducing unnecessary resource waste, and improving protection efficiency. Through the analysis of time series data and the identification of change points, the security protection system can dynamically respond to changing security threats, ensuring that the system can adapt to changes in different attack scenarios and maintain efficient protection capabilities. Accurate location of changing data and threat screening reduce false alarm rates, avoid unnecessary security alerts, and improve the effectiveness and credibility of the security protection system.
本实施例中，获取模拟攻击金融信息中的所有数据点，通过时间序列分析方法，将这些数据点按照时间顺序重新排列。利用时序数据挖掘技术，例如滑动窗口算法，将模拟攻击金融信息中的特征数据转换为连续的时序数据，形成有序的时间序列，对时间维度进行提取与转换，确保每一个数据点都反映出其在时间轴上的演化趋势，时序转换完成后，得到的模拟时序数据将用于后续的变化趋势分析。针对生成的模拟时序数据，使用趋势分析工具进行深度分析，通过应用自回归积分滑动平均模型（ARIMA）或霍尔特-温特斯指数平滑法等时间序列分析算法，分析数据中的变化趋势，捕捉到数据在不同时间段内的增长、衰退、波动等特征，分析过程中，对趋势的显著性进行统计检验，以保证分析结果的可靠性，将分析结果汇总，生成一条完整的防护变化趋势线。根据生成的防护变化趋势，使用变化点检测算法，如累积和控制图（CUSUM）或贝叶斯变化点检测方法，逐步扫描模拟时序数据，定位出数据发生显著变化的时间点，每个变化点都表示存在的安全威胁或异常活动的开始时间，结合趋势线的斜率变化和数据波动的幅度，进行多层次的定位与验证，确保每一个识别出的变化点都具有实际的安全意义，定位完成后，将所有变化点汇总，作为后续威胁筛查的基础数据。利用安全数据变化点对模拟攻击金融信息进行全面的安全威胁筛查。筛查过程采用基于规则的自动化威胁检测工具，结合机器学习模型进行细粒度分析，具体操作包括将每个变化点的数据特征与已知威胁模式进行对比匹配，评估其威胁级别，使用异常检测算法对变化点附近的数据进行深入分析，以识别潜在的未知威胁。In this embodiment, all data points in the simulated attack financial information are obtained, and these data points are rearranged in chronological order through the time series analysis method. Time series data mining technology, such as sliding window algorithm, is used to convert the characteristic data in the simulated attack financial information into continuous time series data to form an ordered time series, and the time dimension is extracted and converted to ensure that each data point reflects its evolution trend on the time axis. After the time series conversion is completed, the obtained simulated time series data will be used for subsequent change trend analysis. For the generated simulated time series data, trend analysis tools are used for in-depth analysis. By applying time series analysis algorithms such as autoregressive integrated moving average model (ARIMA) or Holt-Winters exponential smoothing method, the change trend in the data is analyzed to capture the growth, decline, fluctuation and other characteristics of the data in different time periods. During the analysis process, the significance of the trend is statistically tested to ensure the reliability of the analysis results. The analysis results are summarized to generate a complete protection change trend line. According to the generated protection change trend, change point detection algorithms such as cumulative sum control chart (CUSUM) or Bayesian change point detection method are used to gradually scan the simulated time series data to locate the time points when the data changes significantly. Each change point indicates the start time of the existing security threat or abnormal activity. Combined with the slope change of the trend line and the amplitude of data fluctuation, multi-level positioning and verification are carried out to ensure that each identified change point has practical security significance. After positioning is completed, all change points are summarized as basic data for subsequent threat screening. Comprehensive security threat screening of simulated attack financial information is carried out using security data change points. The screening process uses rule-based automated threat detection tools combined with machine learning models for fine-grained analysis. Specific operations include comparing and matching the data features of each change point with known threat patterns, evaluating its threat level, and using anomaly detection algorithms to conduct in-depth analysis of data near the change point to identify potential unknown threats.
优选地，步骤S6包括：Preferably, step S6 comprises:
步骤S61：根据安全威胁数据对加密金融信息进行风险分层，得到多层级风险子块；Step S61: Risk stratify the encrypted financial information according to the security threat data to obtain multi-level risk sub-blocks;
步骤S62：对多层级风险子块进行循环同态加密，得到同态加密子块；Step S62: performing cyclic homomorphic encryption on the multi-level risk sub-blocks to obtain homomorphically encrypted sub-blocks;
步骤S63：基于同态加密子块对安全威胁数据进行随机嵌套加密，生成多层加密金融信息；Step S63: Perform random nested encryption on the security threat data based on the homomorphic encryption sub-block to generate multi-layer encrypted financial information;
步骤S64：对多层加密金融信息进行逻辑封装处理，生成重加密金融信息。Step S64: logically encapsulate the multi-layer encrypted financial information to generate re-encrypted financial information.
本发明通过根据安全威胁数据对加密金融信息进行风险分层，得到多层级风险子块，能够将金融信息按风险等级进行精细划分，有助于对不同级别的风险采取针对性的防护措施，提高风险管理的精确度。对多层级风险子块进行循环同态加密，得到同态加密子块，循环同态加密技术允许在不解密的情况下对数据进行操作，既保障了数据的安全性，又支持数据的安全计算和分析。基于同态加密子块对安全威胁数据进行随机嵌套加密，生成多层加密金融信息，进一步提升了加密数据的复杂性和安全性，增加了攻击者破解的难度，有效防止数据泄露。通过多层级的加密方式，增加了数据的防护层级，使得即使部分加密层被突破，仍然有其他加密层提供保护，有效抵御了复杂攻击，提高了整体数据安全性。对多层加密金融信息进行逻辑封装处理，生成重加密金融信息，确保了加密数据在传输和存储过程中的完整性和不可篡改性，为数据提供了更高的安全保障。通过对不同风险级别的数据进行分层加密和封装处理，可以动态适应不同的安全需求和风险情景，提升了金融信息安全防护的适应性和灵活性。通过实施多层次加密和逻辑封装策略，系统整体的安全性得到显著提升，能够有效应对各种潜在的安全威胁。The present invention performs risk stratification on encrypted financial information according to security threat data to obtain multi-level risk sub-blocks, which can finely divide financial information according to risk levels, help to take targeted protective measures for risks of different levels, and improve the accuracy of risk management. The multi-level risk sub-blocks are cyclically homomorphically encrypted to obtain homomorphically encrypted sub-blocks. The cyclic homomorphic encryption technology allows data to be operated without decryption, which not only ensures the security of the data, but also supports the secure calculation and analysis of the data. Based on the homomorphically encrypted sub-blocks, the security threat data is randomly nested and encrypted to generate multi-layer encrypted financial information, which further improves the complexity and security of the encrypted data, increases the difficulty of cracking by attackers, and effectively prevents data leakage. Through the multi-level encryption method, the protection level of the data is increased, so that even if some encryption layers are broken, there are still other encryption layers to provide protection, effectively resisting complex attacks and improving the overall data security. The multi-layer encrypted financial information is logically encapsulated to generate re-encrypted financial information, which ensures the integrity and non-tamperability of the encrypted data during transmission and storage, and provides higher security for the data. By performing layered encryption and encapsulation of data at different risk levels, it can dynamically adapt to different security needs and risk scenarios, improving the adaptability and flexibility of financial information security protection. By implementing multi-level encryption and logical encapsulation strategies, the overall security of the system has been significantly improved, and it can effectively respond to various potential security threats.
本实施例中，通过分析安全威胁数据，对加密金融信息中的不同数据部分进行风险评估，采用加权评分法或风险矩阵，对每个数据部分的风险等级进行量化，生成一个包含高、中、低不同风险等级的分层结构，利用递归分区算法，将加密金融信息按照评估结果进行分割，形成多个具有独立风险等级的风险子块，每个风险子块包含的内容都依据其对应的威胁程度和影响范围进行严格划分，确保在后续的处理过程中能够针对性地进行不同层级的保护和加密。使用循环同态加密算法对每个子块进行加密处理，具体步骤包括为每一个风险子块生成对应的公钥和私钥对，并根据风险等级动态调整同态加密的参数，例如加密多项式的度数或模数的大小，逐个应用这些加密参数对风险子块的数据进行循环加密，确保加密后的数据在保持可操作性的同时，实现不同风险层级的安全隔离，每个子块经过加密后，生成对应的同态加密子块，利用这些加密子块对安全威胁数据进行随机嵌套加密，操作过程中，通过随机数生成器生成一组嵌套加密顺序，并基于此顺序逐层对安全威胁数据进行加密处理，将安全威胁数据分割成若干个部分，并将每个部分分别嵌套在对应的同态加密子块内进行初次加密，将已嵌套加密的数据进一步进行多层次的加密处理，确保在加密链中，每层加密均使用不同的同态加密子块及参数，形成一个复杂且难以破解的多层加密结构，最终得到的多层加密金融信息在安全性上达到较高的防护标准，能够有效抵御多种类型的攻击。利用编排逻辑算法对加密信息进行结构优化，减少冗余信息，优化加密层次的分布，采用数据封装技术对加密信息的逻辑结构进行整合，形成一个封闭的加密体系，通过设定访问控制策略和加密信息的调用规则，进一步增强信息的防护能力，生成重加密金融信息，并确保该数据在逻辑封装后的状态下仍然能够通过特定的解密路径进行访问和操作，从而达到既保证安全性又维持数据可用性的目标。In this embodiment, by analyzing security threat data, risk assessment is performed on different data parts in the encrypted financial information, and a weighted scoring method or risk matrix is used to quantify the risk level of each data part, generating a hierarchical structure containing high, medium and low risk levels. Using a recursive partitioning algorithm, the encrypted financial information is segmented according to the assessment results to form multiple risk sub-blocks with independent risk levels. The content of each risk sub-block is strictly divided according to its corresponding threat level and impact range, ensuring that different levels of protection and encryption can be carried out in a targeted manner in the subsequent processing process. Each sub-block is encrypted using a cyclic homomorphic encryption algorithm. The specific steps include generating a corresponding public key and private key pair for each risk sub-block, and dynamically adjusting the parameters of the homomorphic encryption according to the risk level, such as the degree of the encryption polynomial or the size of the modulus. These encryption parameters are applied one by one to cyclically encrypt the data of the risk sub-blocks to ensure that the encrypted data can be operated while achieving security isolation of different risk levels. After each sub-block is encrypted, a corresponding homomorphic encryption sub-block is generated, and these encrypted sub-blocks are used to perform random nested encryption on the security threat data. During the operation, a set of nested encryption sequences is generated by a random number generator, and the security threat data is encrypted layer by layer based on this sequence. The security threat data is divided into several parts, and each part is nested in the corresponding homomorphic encryption sub-block for initial encryption. The nested encrypted data is further encrypted at multiple levels to ensure that in the encryption chain, each layer of encryption uses different homomorphic encryption sub-blocks and parameters to form a complex and difficult-to-crack multi-layer encryption structure. The resulting multi-layer encrypted financial information reaches a high level of protection in terms of security and can effectively resist various types of attacks. The structure of encrypted information is optimized by using orchestration logic algorithms, redundant information is reduced, and the distribution of encryption levels is optimized. The logical structure of encrypted information is integrated using data encapsulation technology to form a closed encryption system. By setting access control policies and calling rules for encrypted information, the protection capabilities of information are further enhanced, and heavily encrypted financial information is generated. It is ensured that the data can still be accessed and operated through specific decryption paths in the logically encapsulated state, thereby achieving the goal of both ensuring security and maintaining data availability.
本发明还提供一种基于人工智能的金融信息安全防护系统，用于执行如上所述的基于人工智能的金融信息安全防护方法，该基于人工智能的金融信息安全防护系统包括：The present invention also provides an artificial intelligence-based financial information security protection system, which is used to execute the artificial intelligence-based financial information security protection method as described above. The artificial intelligence-based financial information security protection system includes:
信息获取模块，用于获取多模态金融信息；对多模态金融信息进行模态类型识别，并进行信息块分组，得到金融交易行为信息块和用户基础信息块；The information acquisition module is used to acquire multimodal financial information; identify the modal type of the multimodal financial information, and group the information blocks to obtain the financial transaction behavior information block and the user basic information block;
行为预测模块，用于对金融交易行为信息块进行行为模式分析，并进行行为趋势预测，生成预测行为数据；The behavior prediction module is used to analyze the behavior patterns of the financial transaction behavior information blocks, predict the behavior trends, and generate predicted behavior data;
信息映射模块，用于基于预测行为数据对用户基础信息块进行层次关联映射，得到用户行为共识数据；The information mapping module is used to perform hierarchical association mapping on the user basic information blocks based on the predicted behavior data to obtain the user behavior consensus data;
联合加密模块，用于利用用户行为共识数据对多模态金融信息进行动态联合加密，生成加密金融信息；Joint encryption module, used to dynamically joint encrypt multimodal financial information using user behavior consensus data to generate encrypted financial information;
威胁筛查模块，用于基于预测行为数据对加密金融信息进行漏洞攻击模拟，并进行安全威胁筛查，得到安全威胁数据；A threat screening module is used to simulate vulnerability attacks on encrypted financial information based on predicted behavior data, and to perform security threat screening to obtain security threat data;
二次加密模块，用于基于安全威胁数据对加密金融信息进行二次加密，生成重加密金融信息。The secondary encryption module is used to perform secondary encryption on encrypted financial information based on security threat data to generate re-encrypted financial information.
本发明通过利用信息获取模块能够识别和分类不同类型的多模态金融信息，将复杂的金融数据分割为行为信息块和基础信息块，增强了系统在处理多维数据时的精度与效率，行为预测模块通过分析金融交易行为模式，进行趋势预测，提前识别潜在风险和异常行为，提升了系统的前瞻性与响应能力，信息映射模块利用预测行为数据进行层次关联映射，生成用户行为共识数据，使得用户基础信息与行为信息之间的关联更为紧密和精准，为后续的加密和防护提供了坚实基础，联合加密模块通过动态联合加密技术，根据用户行为共识数据实时调整加密策略，使得加密过程更加灵活，能够有效应对不断变化的安全威胁，威胁筛查模块通过模拟漏洞攻击并进行安全威胁筛查，及时发现加密信息中的潜在威胁，有助于提前识别和预防安全漏洞，增强系统的整体防护能力，二次加密模块在初次加密的基础上，结合安全威胁数据进行二次加密，进一步提升了金融信息的保密性和安全性，确保在面对复杂的攻击时仍能保持高强度的防护。The present invention can identify and classify different types of multimodal financial information by utilizing the information acquisition module, divide complex financial data into behavioral information blocks and basic information blocks, and enhance the accuracy and efficiency of the system in processing multidimensional data. The behavioral prediction module analyzes the financial transaction behavior pattern, performs trend prediction, and identifies potential risks and abnormal behaviors in advance, thereby improving the system's foresight and responsiveness. The information mapping module uses the predicted behavior data to perform hierarchical association mapping and generates user behavior consensus data, so that the association between user basic information and behavior information is closer and more accurate, providing a solid foundation for subsequent encryption and protection. The joint encryption module uses dynamic joint encryption technology to adjust the encryption strategy in real time according to the user behavior consensus data, making the encryption process more flexible and able to effectively respond to changing security threats. The threat screening module simulates vulnerability attacks and performs security threat screening to timely discover potential threats in encrypted information, which helps to identify and prevent security vulnerabilities in advance and enhance the overall protection capability of the system. The secondary encryption module performs secondary encryption in combination with security threat data on the basis of the initial encryption, further improving the confidentiality and security of financial information, and ensuring that high-intensity protection can be maintained in the face of complex attacks.
因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在申请文件的等同要件的含义和范围内的所有变化涵括在本发明内。Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.
以上所述仅是本发明的具体实施方式，使本领域技术人员能够理解或实现本发明。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所发明的原理和新颖特点相一致的最宽的范围。The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.
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| CN115813367A (en)* | 2022-11-29 | 2023-03-21 | 深圳先进技术研究院 | Multimodal brain network calculation method, device, equipment and medium of structure-function correlation |
| CN118035463A (en)* | 2024-01-22 | 2024-05-14 | 国网安徽省电力有限公司电力科学研究院 | Method and system for constructing power grid dispatching multi-mode knowledge graph |
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| US20210191506A1 (en)* | 2018-01-26 | 2021-06-24 | Institute Of Software Chinese Academy Of Sciences | Affective interaction systems, devices, and methods based on affective computing user interface |
| US20220261668A1 (en)* | 2021-02-12 | 2022-08-18 | Tempus Labs, Inc. | Artificial intelligence engine for directed hypothesis generation and ranking |
| CN115813367A (en)* | 2022-11-29 | 2023-03-21 | 深圳先进技术研究院 | Multimodal brain network calculation method, device, equipment and medium of structure-function correlation |
| CN118035463A (en)* | 2024-01-22 | 2024-05-14 | 国网安徽省电力有限公司电力科学研究院 | Method and system for constructing power grid dispatching multi-mode knowledge graph |
| CN118094551A (en)* | 2024-04-24 | 2024-05-28 | 深圳市时代经纬科技有限公司 | System security analysis method, device and medium based on big data |
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| CN119272314A (en)* | 2024-12-11 | 2025-01-07 | 北京霍因科技有限公司 | Sensitive data protection method and device |
| CN119299454A (en)* | 2024-12-13 | 2025-01-10 | 深圳桑达银络科技有限公司 | Secure transaction service terminal based on AI cloud platform |
| CN119939600A (en)* | 2025-01-03 | 2025-05-06 | 安徽大学 | Code vulnerability detection method and vulnerability detection system based on graph simplification |
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| EE01 | Entry into force of recordation of patent licensing contract | Application publication date:20241029 Assignee:Youdi Defender (Tianjin) Information Technology Co.,Ltd. Assignor:CANGZHOU NORMAL University Contract record no.:X2025980007807 Denomination of invention:A financial information security protection method and system based on artificial intelligence Granted publication date:20250107 License type:Common License Record date:20250425 |