Author:
Wang Yuanyuan,Li Gengwang,Yu Min,Chow Kam-Pui,Jiang Jianguo,Meng Xiang,Huang Weiqing
Publisher
Springer Nature Switzerland
Reference31 articles.
1. A. Bozkir, E. Tahillioglu, M. Aydos and I. Kara, Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision, Computers and Security, vol. 103, article no. 102166, 2021.
2. A. Cohen, N. Nissim, L. Rokach and Y. Elovici, SFEM: Structural feature extraction methodology for the detection of malicious Office documents using machine learning methods, Expert Systems with Applications, vol. 63, pp. 324–343, 2016.
3. I. Corona, D. Maiorca, D. Ariu and G. Giacinto, Lux0R: Detection of malicious PDF-embedded JavaScript code through discriminant analysis of API references, Proceedings of the Workshop on Artificial Intelligence and Security, pp. 47–57, 2014.
4. M. Cova, C. Kruegel and G. Vigna, Detection and analysis of drive-by-download attacks and malicious JavaScript code, Proceedings of the Nineteenth International Conference on the World Wide Web, pp. 281–290, 2010.
5. C. Curtsinger, B. Livshits, B. Zorn and C. Seifert, ZOZZLE: Fast and precise in-browser JavaScript malware detection, Proceedings of the Twentieth USENIX Security Symposium, 2011.