A Survey Paper on Malware Detection Techniques
J Zhao, S Zhang, B Liu, B Cui, Aslan, R Samet, Y Gao, Z Lu, Y Luo, F Cohen, D Chess, S White, F Cohen, L Adleman, D Spinellis, Z Zuo, Q Zhu, M Zhou, M Zolkipli, A Jantan, Y Tang, B Xiao, X Lu, H Borojerdi, M Abadi, J Newsome, B Karp, D Song, L Adkins, M Jones, J Carlisle, Upchurch, Y Ye, T Li, Q Jiang, Y Wang, Y Ye, D Wang, T Li, D Ye, Q Jiang, Z Bazrafshan, H Hashemi, S Fard, A Hamzeh, D Bilar, R Islam, R Tian, L Batten, S Versteeg, E Gandotra, D Bansal, S Sofat, Tavallaee, D Arp, M Spreitzenbarth, M Hbner, H Gascon, K Rieck, Drebin, A Lashkari, A Kadir, H Gonzalez, K Mbah, . Ghorbani, D Gavrilu, M Cimpoeu, D Anton, L Ciortuz, J Zhao, S Zhang, B Liu, B Cui
Abstract
The invasion of machine learning on various field in engineering in recent days is quite astonishing. The recent growth in new malwares have put a burden on our traditional anti malwares that use signature based or heuristic based techniques to detect malwares as these either cannot detect zero-day malwares or it would be insufficient to detect a certain type of malware. So, we need to find some new technique to deal with this situation. In this survey paper we shall look into how machine learning can potentially be used as an anti-malware