Malware Analysis and Classification Using Machine Learning Models

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Author(s): Swadeep Swadeep (Vellore Institute of Technology, Chennai, India), Karmel Arockiasamy (Vellore Institute of Technology, Chennai, India)and Karthika Perumal (Vellore Institute of Technology, Chennai, India)
Copyright: 2024
Pages: 12
Source title: Machine Learning Algorithms Using Scikit and TensorFlow Environments
Source Author(s)/Editor(s): Puvvadi Baby Maruthi (Dayananda Sagar University, India), Smrity Prasad (Dayananda Sagar University, India)and Amit Kumar Tyagi ( National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/978-1-6684-8531-6.ch010

Keywords: Engineering Science Reference / Knowledge Management and Library Science / Machine Learning / Systems and Software Engineering

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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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Malware Analysis and Classification Using Machine Learning Models

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