Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach

doi:10.23940/ijpe.24.05.p6.312318

Abstract

Abstract: One of the most popular OSs utilized by the public these days is Windows. A serious concern to the security and integrity of Windows OS systems is the proliferation of malware. The goal of this research project is to create a practical method for identifying and categorizing various malware kinds on the Windows operating system to combat the pervasive malware problem. For the efficient identification and classification of malware on Windows, an ensemble technique using hybridization of Support Vector Machine, Decision tree, and Logistic Regression is proposed. The suggested method makes use of the idea of feature selection methods to determine the patterns and signatures of numerous malware families. The genuine malware dataset will be used to test and assess the suggested ensemble as well as the current basic machine learning techniques. In the end, this will assist both the novice and the expert in cyber security to comprehend and prepare for the ever-changing threats posed by the new breed of malware on Windows PCs.

Key words: malware classification, machine learning, information security, security analysis, windows os

Vikas Verma, Arun Malik, and Isha Batra. Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach [J]. Int J Performability Eng, 2024, 20(5): 312-318.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Chaubey V.P., Sharma A., Sharma T., Sharma S. and Kumar A., 2023, November. Link Prediction for Social Network Analysis Using Random Forest and XG-Boost Algorithm. In 2023 Second International Conference on Informatics (ICI)(pp. 1-6). IEEE.
[2] Aslan, Ö.A. and Samet, R., 2020. A comprehensive review on malware detection approaches.IEEE access, 8, pp.6249-6271.
[3] Cyber Once, What are the different types of Malware?, https://cyberone.security/what-are-the-different-types-of-malware/, accessed on May 1, 2024.
[4] Shamneesh S., Manoj M. and Keshav K., 2020. Node-level self-adaptive network path restructuring technique for Internet of things (IoT). In Intelligent Communication, Control and Devices: Proceedings of ICICCD 2018(pp. 453-461). Springer Singapore.
[5] Sharma, S. and Mishra, N., 2023. Horizoning recent trends in the security of smart cities: Exploratory analysis using latent semantic analysis. Journal of Intelligent & Fuzzy Systems, (Preprint), pp.1-18.
[6] Shamneesh, S. and Mishra, N., 2023. Integration of Blockchain Technology into Smart Cities for Information Security: Research Constituents and Need of Research. In7th International Joint Conference on Computing Sciences (ICCS-2023).
[7] Lutkevich B.,Definition: malware, https://www.techtarget.com/searchsecurity/definition/malware, accessed on May 1, 2024.
[8] Naz, S. and Singh, D.K., 2019, July. Review of machine learning methods for windows malware detection. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)(pp. 1-6). IEEE.
[9] Srikanth B., Sharma S., Chaubey V.P. and Kumar A., 2023, August. Forecasting the Prices using Machine Learning Techniques: Special Reference to used Mobile Phones. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)(pp. 503-508). IEEE.
[10] Shiva Darshan, S.L. and Jaidhar, C.D., 2019. Windows malware detection system based on LSVC recommended hybrid features.Journal of Computer Virology and Hacking Techniques, 15, pp.127-146.
[11] Alsmadi, T. and Alqudah, N., 2021, July. A survey on malware detection techniques. In 2021 international conference on information technology (ICIT)(pp. 371-376). IEEE.
[12] Lad, S.S. and Adamuthe, A.C., 2022. Improved deep learning model for static PE files malware detection and classification.International Journal of Computer Network and Information Security, 13(2), p.14.
[13] Mane T., Nimase P., Parihar P. and Chandankhede P., 2022. Review of malware detection using deep learning. In Soft Computing for Security Applications: Proceedings of ICSCS 2021(pp. 255-262). Springer Singapore.
[14] Aboaoja F.A., Zainal A., Ghaleb F.A., Al-Rimy B.A.S., Eisa T.A.E. and Elnour A.A.H., 2022. Malware detection issues, challenges, and future directions: A survey.Applied Sciences, 12(17), p.8482.
[15] Jing C., Wu Y. and Cui C., 2022. Ensemble dynamic behavior detection method for adversarial malware.Future Generation Computer Systems, 130, pp.193-206.
[16] Chai Y., Du L., Qiu J., Yin L. and Tian Z., 2022. Dynamic prototype network based on sample adaptation for few-shot malware detection. IEEE Transactions on Knowledge and Data Engineering,35(5), pp.4754-4766.
[17] Singh, J. and Singh, J., 2022. Assessment of supervised machine learning algorithms using dynamic API calls for malware detection. International Journal of Computers and Applications,44(3), pp.270-277.