Detection of Malware by Static Analysis Using Machine Learning Methods
Abstract
The increase in cyber-attacks has also started to threaten the use of internet and information technologies. This situation emphasizes the importance of detecting malicious software that is responsible for cyber-attacks. Nowadays, there are studies on the development of machine learning methods for malicious software detection. Malicious software detectors are the primary tools in defense against malicious software. The quality of such a detector is determined by the techniques it uses. Malware analysis methods such as machine learning, deep learning, and static and dynamic analysis are among these techniques. This study presents malware analysis and classification techniques. For malware detection, well-known algorithms for machine learning including such K-Nearest Neighbors, Naive Bayes, Decision Trees, and Random Forest were used. The research shows that the use of Random Forest classification technique produces the best accuracy with 97.75% classification, while Naive Bayes produces the lowest accuracy of 53%.
Keywords
Cyber security, Malware detection, Malware analysis, Machine learning
References
- Azeez, N. A., Odufuwa, O. E., Misra, S., Oluranti, J., & Damaševičius, R. (2021). Windows PE malware detection using ensemble learning. In Informatics (Vol. 8, No. 1, p. 10). MDPI.
- Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
- Chumachenko, K. (2017). Machine learning methods for malware detection and classification.
- Deshpande, N. M., Gite, S., & Aluvalu, R. (2021). A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Computer Science, 7, e460.
- Gandotra, E., Bansal, D., & Sofat, S. (2014). Malware analysis and classification: A survey. Journal of Information Security, 2014.
- Harshalatha, P., & Mohanasundaram, R. (2020). Classification Of Malware Detection Using Machine Learn-ing Algorithms: A Survey. International Journal of Scientific & Technology Research, 9(02).
- Hassen, M., Carvalho, M. M., & Chan, P. K. (2017, November). Malware classification using static analysis based features. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
- Maimon, O., & Rokach, L. (Eds.). (2005). Data mining and knowledge discovery handbook.
- Markel, Z., & Bilzor, M. (2014, October). Building a machine learning classifier for malware detection. In 2014 second workshop on anti-malware testing research (WATeR) (pp. 1-4). IEEE.
- Patil, R., & Deng, W. (2020, March). Malware analysis using machine learning and deep learning techniques. In 2020 SoutheastCon (Vol. 2, pp. 1-7). IEEE.