Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors

Praveen Hugar; Mayur Pershad; T. Sathvika; Ganesh Bhukya

doi:10.55524/ijirem.2022.9.3.6

Abstract

Malware is any programme that gains access to or instals itself on a computer without the permission of the system's administrators. For cyber-criminals to achieve their nefarious objectives and purposes, a variety of viruses has been widely deployed. To tackle the growing number of malicious programmes and lessen their hazard, a novel deep learning framework is developed that employs NLP approaches as a starting point and combines CNN and LSTM neurones to record locally spatial correlations and learn from sequential longterm dependencies. As a result, for the malware classification job, high-level abstractions and representations are automatically derived. The accuracy of categorization rises from 0.81 (best by Random Forest) to about 1.0.

Keywords

Machine Learning, Computer Security, Malware Recognition.

References

[1] Nai Ding, Lucia Melloni, Xing Tian, and David Poeppel. Rulebased and word-level statistics-based processing of language: insights from neuroscience. Language, Cognition and Neuroscience, 32(5):570–575, 2017.

[2] Manuel Egele, Theodoor Scholte, Engin Kirda, and Christopher Kruegel. A survey on automated dynamic malware-analysis techniques and tools. ACM computing surveys (CSUR), 44(2):6, 2012.

[3] Ekta Gandotra, Divya Bansal, and Sanjeev Sofat. Malware analysis and classification: A survey. Journal of Information Security, 5(02):56, 2014.

[4] Rafiqul Islam, Ronghua Tian, Lynn M Batten, and Steve Versteeg. Classification of malware based on integrated static and dynamic features. Journal of Network and Computer Applications, 36(2):646–656, 2013.

[5] Judith Klein-Seetharaman Madhavi Ganapathiraju, Vijayalaxmi Manoharan. Blmt - statistical sequence analysis using n-grams. Applied Bioinformatics, 3(2-3):193–200, 2004.

[6] Igor Santos, Jaime Devesa, Felix Brezo, Javier Nieves, and Pablo Garcia Bringas. Opem: A static-dynamic approach for machine-learning-based malware detection. In International Joint Conference CISIS’12-ICEUTE 12-SOCO 12 Special Sessions, pages 271–280. Springer, 2013.

[7] Tzu-Yen Wang, Shi-Jinn Horng, Ming-Yang Su, Chin-Hsiung Wu, PengChu Wang, and Wei-Zen Su. A surveillance spyware detection system based on data mining methods. In Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pages 3236– 3241. IEEE, 2006.

Cites this article as

P. Hugar, M. Pershad, T. Sathvika, G. Bhukya, "Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-9, Issue-3, Page No-41-44, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.3.6