Fan et al., 2016
ViewPDF| Publication | Publication Date | Title |
|---|---|---|
| Fan et al. | Malicious sequential pattern mining for automatic malware detection | |
| Aslan et al. | A new malware classification framework based on deep learning algorithms | |
| Bazrafshan et al. | A survey on heuristic malware detection techniques | |
| Islam et al. | Classification of malware based on string and function feature selection | |
| Liangboonprakong et al. | Classification of malware families based on n-grams sequential pattern features | |
| Alazab et al. | A hybrid wrapper-filter approach for malware detection | |
| Sun et al. | An opcode sequences analysis method for unknown malware detection | |
| Li et al. | An adversarial machine learning method based on opcode n-grams feature in malware detection | |
| San et al. | Malicious software family classification using machine learning multi-class classifiers | |
| Alazab et al. | Detecting malicious behaviour using supervised learning algorithms of the function calls | |
| Shenderovitz et al. | Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls | |
| Hou et al. | Cluster-oriented ensemble classifiers for intelligent malware detection | |
| He et al. | Detection of Malicious PDF Files Using a Two‐Stage Machine Learning Algorithm | |
| Okane et al. | Malware detection: program run length against detection rate | |
| Khammas | Malware detection using sub-signatures and machine learning technique | |
| Masabo et al. | Improvement of malware classification using hybrid feature engineering | |
| Darshan et al. | An empirical study to estimate the stability of random forest classifier on the hybrid features recommended by filter based feature selection technique | |
| Khammas et al. | Accuracy improved malware detection method using snort sub-signatures and machine learning techniques | |
| Hammi et al. | Malware detection through windows system call analysis | |
| Ni et al. | FindMal: A file-to-file social network based malware detection framework | |
| Nar et al. | Analysis and comparison of opcode-based malware detection approaches | |
| Soliman et al. | Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier. | |
| Sun et al. | MLxPack: Investigating the effects of packers on ML-based Malware detection systems using static and dynamic traits | |
| Kumar et al. | A survey of deep learning techniques for malware analysis | |
| Liu et al. | FENOC: An ensemble one-class learning framework for malware detection |