A Survey on Different Approaches for Malware Detection Using Machine Learning Techniques
Malwares are increasing in volume and variety, by posing a big threat to digital world and is one of the major alarms over the past few years for the security in industries. They can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures. Traditional Anti-Intrusion Detection/Intrusion prevention system and anti-virus softwares follow signature based methods which makes the detection of unknown or zero day malwares almost impossible. This issue can be solved by more sophisticated mechanisms in which, static and dynamic malware analysis can be used together with machine learning algorithms for classifying and detecting malware. Through this paper we present a survey on the different techniques for concealment and obfuscation used to make sophisticated malware as well as the different approaches used in malware detection and analysis.
KeywordsMalware analysis CyberSecurity Machine learning
- 2.Beaucamps, P.: Advanced polymorphic techniques. Int. J. Comput. Sci. 2(3), 194–205 (2007)Google Scholar
- 4.Govindaraju, A.: Exhaustive statistical analysis for detection of metamorphic malware. [MS Project], San Jose State University, US (2010)Google Scholar
- 10.Boujnouni, M.E., Jedra, M., Zahid, N.: New malware detection framework based on N-grams and support vector domain description. In: 2015 11th International Conference on Information Assurance and Security (IAS), pp. 123–128 (2015)Google Scholar
- 15.Norouzi, M., Souri, A., Samad Zamini, M.: A data mining classification approach for behavioral malware detection. J. Comput. Netw. Commun. 2016, 9 (2016)Google Scholar
- 18.Dali, Z., Hao, J., Ying, Y., Wu, D., Weiyi, C.: DeepFlow: deep learning-based malware detection by mining Android application for abnormal usage of sensitive data. In: 2017 IEEE Symposium on Computers and Communications (ISCC), pp 438–443 (2017)Google Scholar