Taxonomy-Based Intelligent Malware Detection Framework

Ali Mirza, Qublai Khan ORCID: 0000-0003-3403-2935, Hussain, F., Awan, Irfan, Younas, M. and Sharieh, S. (2020) Taxonomy-Based Intelligent Malware Detection Framework. In: IEEE Global Communications Conference: Revolutionizing Communications, 9-13 December 2019, Waikoloa, HI, USA. ISSN 2576-6813

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Abstract

Timely detection of a malicious piece of code accurately, in an enterprise network or in an individual device, before it propagates and mutate itself, is one of the most challenging tasks in the domain of cyber security. Millions of variants of each latest malware are released every day and each of these variants have a unique static signature. Conventional anti-malware tools use signatures and static heuristics of malware to segregate them from legitimate files, which is not an effective technique because of the number of malware variants released every passing day. To overcome the fundamental flaw of operational techniques, we propose a framework that generalizes the static and dynamic malware features that are used to train multiple machine learning algorithms. The generalization of clean and malicious features enables the framework to accurately differentiate between clean and malicious files.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Malware; ML and Malware Detection; Malware Analysis; Machine Learning
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Kate Greenaway
Date Deposited:	03 Jul 2020 15:23
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/8527

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