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Behaviour-aware Malware Classification: Dynamic Feature Selection

Vu Dinh, P, Shone, N, Phan Huy, D, Shi, Q, Nguyen Viet, H and Tran Nguyen, N (2019) Behaviour-aware Malware Classification: Dynamic Feature Selection. In: 2019 11th International Conference on Knowledge and Systems Engineering (KSE) . (11th International Conference on Knowledge and Systems Engineering (KSE), 24 October 2019 - 26 October 2019, Da Namg, Vietnam).

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Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. The results achieved thus far have demonstrated promising improvements over existing approaches.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 22 Nov 2019 10:01
Last Modified: 20 May 2024 13:16
DOI or ID number: 10.1109/KSE.2019.8919491
URI: https://researchonline.ljmu.ac.uk/id/eprint/11669
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