Yan et al., 2020
| Publication | Publication Date | Title |
|---|---|---|
| Yan et al. | Trustworthy network anomaly detection based on an adaptive learning rate and momentum in IIoT | |
| Sun et al. | DL‐IDS: Extracting Features Using CNN‐LSTM Hybrid Network for Intrusion Detection System | |
| Injadat et al. | Multi-stage optimized machine learning framework for network intrusion detection | |
| Haggag et al. | Implementing a deep learning model for intrusion detection on apache spark platform | |
| Bodström et al. | State of the art literature review on network anomaly detection with deep learning | |
| Kanimozhi et al. | Oppositional tunicate fuzzy C‐means algorithm and logistic regression for intrusion detection on cloud | |
| Maseer et al. | Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges | |
| Lan et al. | E‐minBatch GraphSAGE: An Industrial Internet Attack Detection Model | |
| Chou et al. | Data-driven network intrusion detection: A taxonomy of challenges and methods | |
| Abusnaina et al. | Subgraph-based adversarial examples against graph-based IoT malware detection systems | |
| Pandey | Design and performance analysis of various feature selection methods for anomaly‐based techniques in intrusion detection system | |
| Gu et al. | Learning-based intrusion detection for high-dimensional imbalanced traffic | |
| Pardhi et al. | Classification of malware from the network traffic using hybrid and deep learning based approach | |
| Naif Alatawi | Enhancing intrusion detection systems with advanced machine learning techniques: An ensemble and explainable artificial intelligence (AI) approach | |
| Logeswari et al. | A comprehensive approach to intrusion detection in IoT environments using hybrid feature selection and multi-stage classification techniques | |
| Pourardebil Khah et al. | A hybrid machine learning approach for feature selection in designing intrusion detection systems (IDS) model for distributed computing networks | |
| Chou et al. | Classification of malicious traffic using tensorflow machine learning | |
| Zheng et al. | Fed-UGI: Federated undersampling learning framework with Gini impurity for imbalanced network intrusion detection | |
| Karanam | Is there a Trojan!: Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments | |
| Wang et al. | Deep learning network intrusion detection based on network traffic | |
| Senthilkumar et al. | An Efficient Investigation of Cloud Computing Security with Machine Learning Algorithm | |
| Soni et al. | Optimized Deep Learning-Based Intrusion Detection and Secure, Energy-Efficient Routing in Wireless Sensor Networks | |
| Trifonov et al. | Adaptive Optimization Techniques for Inteligent Network Security | |
| Sama et al. | Enhancing system security by intrusion detection using deep learning | |
| Hossen et al. | Traffic Classification For Botnet Detection Using Deep Learning |