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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Malware strategies and issues in examination

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pp. 1117–1127Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1696XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1696
Pages:
1117–1127

Abstract

The field of information technology is experiencing rapid advancements, presenting significant challenges in security and privacy for cybersecurity professionals. Research indicates a concerning increase in the development of new malware strains. A perpetual cycle exists between the evolution of malicious software and the efforts to safeguard against them. Antivirus companies continually update their malware signatures, while attackers persistently seek ways to evade detection. Machine learning offers promising capabilities for detecting malware, with feature extraction as the initial step and classification as the subsequent phase. The effectiveness of classification algorithms is influenced by the quality of the extracted features. This articles aim is to assist aspiring researchers in selecting suitable algorithms for their investigations.

Keywords

MalwareMachine learningSecurity and privacyCybersecurityEncryption

Subject Classifications

Primary 68T1568T0568T3 Secondary 11T71

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