Abstract
Generally, malware has come to be known as one of the biggest threats, so malware is a program which operates malicious actions and steals information, to specifically identify it as software which is designed specifically to through breaking the system of a computer without consent from the owner. This chapter aimed to study feature selection and malware classification using machine learning. The identification of such features was done through the intuition that various parts of the PE files’ features can correlate with one another less than with the class files, being clean or dirty. Such features are implemented as algorithms in machine learning to help classify the malware, resulting in such classification to be properly implemented in antivirus programs to help enhance the rate of detection.
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References
Khammas, B. M., Monemi, A., Bassi, J. S., Ismail, I., Nor, S. M., & Marsono, M. N. (2015). Feature selection and machine learning classification for malware detection. Jurnal Teknologi, 77. https://doi.org/10.11113/jt.v77.3558.
Gavriluţ, D., Cimpoeşu, M., Anton, D., & Ciortuz, L. (2009). Malware detection using machine learning. In International Multiconference on Computer Science and Information Technology, 2009. IMCSIT’09 (pp. 735–741). IEEE.
Chumachenko, K. (2017). Machine learning methods for malware detection and classification. XAMK University of Applied Science.
Liu, L., Wang, B.-S., Yu, B., & Zhong, Q.-X. (2017). Automatic malware classification and new malware detection using machine learning. Frontiers of Information Technology & Electronic Engineering, 18, 1336–1347.
Raman, K. (2012). Selecting features to classify malware. In InfoSec Southwest 2012.
Vinod, P., Laxmi, V., & Gaur, M. S. (2012). Reform: Relevant features for malware analysis. In 26th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2012 (pp. 738–744). IEEE.
Ismail, I., & Elektrik, F. K. (2013). Naive Bayes classification with domain knowledge for new malware variants and stateless packet level detection. Skudai, Malaysia: Universiti Teknologi Malaysia.
Moskovitch, R., Stopel, D., Feher, C., Nissim, N., Elovici, Y. (2008). Unknown malcode detection via text categorization and the imbalance problem. In IEEE International Conference on Intelligence and Security Informatics, 2008. ISI 2008 (pp. 156–161). IEEE.
Moskovitch, R., Stopel, D., Feher, C., Nissim, N., Japkowicz, N., & Elovici, Y. (2009). Unknown malcode detection and the imbalance problem. Journal in Computer Virology, 5, 295.
Reddy, D. K. S., & Pujari, A. K. (2006). N-gram analysis for computer virus detection. Journal in Computer Virology, 2, 231–239.
Liangboonprakong, C., & Sornil, O. (2013). Classification of malware families based on n-grams sequential pattern features. In 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2013 (pp. 777–782). IEEE.
Lin, C.-T., Wang, N.-J., Xiao, H., & Eckert, C. (2015). Feature selection and extraction for malware classification. Journal of Information Science and Engineering, 31, 965–992.
Singhal, P., & Raul, N. (2012). Malware detection module using machine learning algorithms to assist in centralized security in enterprise networks. International Journal of Network Security & Its Applications, 4, 61.
Baldangombo, U., Jambaljav, N., Horng, S.-J. (2013). A static malware detection system using data mining methods. arXiv preprint arXiv:13082831.
Alazab, M., Venkatraman, S., Watters, P., & Alazab, M. (2011). Zero-day malware detection based on supervised learning algorithms of API call signatures. In Proceedings of the Ninth Australasian Data Mining Conference (Vol. 121, pp. 171–182). Australian Computer Society, Inc..
Siddiqui, M., Wang, M. C., & Lee, J. (2008). Detecting Trojans using data mining techniques. In International Multi Topic Conference (pp. 400–411). Springer.
Schultz, M. G., Eskin, E., Zadok, F., & Stolfo, S. J. (2001). Data mining methods for detection of new malicious executables. In 2001 IEEE Symposium on Security and Privacy, 2001. S&P 2001. Proceedings (pp. 38–49). IEEE.
Shafiq, M. Z., Tabish, S. M., Mirza, F., & Farooq, M. (2009). PE-Miner: Mining structural information to detect malicious executables in realtime. In Recent advances in intrusion detection (pp. 121–141). Berlin: Springer.
Yan, G., Brown, N., & Kong, D. (2013). Exploring discriminatory features for automated malware classification. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 41–61). Springer.
Alkasassbeh, M. (2017). An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods. Journal of Theoretical and Applied Information Technology, 22, 95.
Almseidin, M., Alzubi, M., Kovacs, S., & Alkasassbeh, M. (2017). Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), 14-16 September 2017 (pp. 000277–000282). https://doi.org/10.1109/SISY.2017.8080566.
Alauthaman, M., Aslam, N., Zhang, L., Alasem, R., & Hossain, M. (2018). A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks. Neural Computing and Applications, 29, 991–1004.
Altaher, A., ALmomani, A., Anbar, M., & Ramadass, S. (2012). Malware detection based on evolving clustering method for classification. Scientific Research and Essays, 7, 2031–2036.
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Al-Kasassbeh, M., Mohammed, S., Alauthman, M., Almomani, A. (2020). Feature Selection Using a Machine Learning to Classify a Malware. In: Gupta, B., Perez, G., Agrawal, D., Gupta, D. (eds) Handbook of Computer Networks and Cyber Security. Springer, Cham. https://doi.org/10.1007/978-3-030-22277-2_36
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