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Malware Analysis and Classification Using Machine Learning Models

Malware Analysis and Classification Using Machine Learning Models

Swadeep Swadeep, Karmel Arockiasamy, Karthika Perumal
Copyright: © 2024 |Pages: 12
ISBN13: 9781668485316|ISBN10: 1668485311|ISBN13 Softcover: 9781668485323|EISBN13: 9781668485330
DOI: 10.4018/978-1-6684-8531-6.ch010
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MLA

Swadeep, Swadeep, et al. "Malware Analysis and Classification Using Machine Learning Models." Machine Learning Algorithms Using Scikit and TensorFlow Environments, edited by Puvvadi Baby Maruthi, et al., IGI Global, 2024, pp. 209-220. https://doi.org/10.4018/978-1-6684-8531-6.ch010

APA

Swadeep, S., Arockiasamy, K., & Perumal, K. (2024). Malware Analysis and Classification Using Machine Learning Models. In P. Baby Maruthi, S. Prasad, & A. Tyagi (Eds.), Machine Learning Algorithms Using Scikit and TensorFlow Environments (pp. 209-220). IGI Global. https://doi.org/10.4018/978-1-6684-8531-6.ch010

Chicago

Swadeep, Swadeep, Karmel Arockiasamy, and Karthika Perumal. "Malware Analysis and Classification Using Machine Learning Models." In Machine Learning Algorithms Using Scikit and TensorFlow Environments, edited by Puvvadi Baby Maruthi, Smrity Prasad, and Amit Kumar Tyagi, 209-220. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/978-1-6684-8531-6.ch010

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Abstract

In modern times, it has become common practice for major corporations to utilize computers for storing data. Unfortunately, the frequency of malware attacks has increased, which facilitates unauthorized individuals' access to private information. Analyzing malware has become a critical task in safeguarding information systems against malicious attacks. Therefore, machine learning techniques have become an effective tool for automating investigations using static and dynamic analysis, combining malware with similar behavior into separate families based on proximity. Deep learning techniques improve the accuracy of malware variant detection and classification by building neural networks with more potentially different layers. This research aims to address this issue by training machine learning models using various algorithms on a dataset obtained by performing static and dynamic analysis on both malicious and benign samples. The resulting models were then combined to produce superior results compared to those obtained from a single model, which can be seen in the results.

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