Staff View: A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection

A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection

As cyber threats continue to evolve in complexity, traditional malware detection methods often fall short in identifying sophisticated attacks. Multimodal analysis, which integrates various data sources and analytical methods, has emerged as a promising approach to enhance malware detection capabili...

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Main Authors:	Ibrahim Z.-A., Ismail S.A., Rahim F.A.
Other Authors:	57203863738
Format:	Conference paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:	Analysis techniques Cyber security Cyber threats Detection methods Malware detection Multi-modal Multi-modal analyze Multi-modal approach Multimodal analysis Systematic literature review Cyber attacks
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id	my.uniten.dspace-36835
record_format	dspace
spelling	my.uniten.dspace-368352025-03-03T15:45:02Z A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection Ibrahim Z.-A. Ismail S.A. Rahim F.A. 57203863738 56943570600 57350579500 Analysis techniques Cyber security Cyber threats Detection methods Malware detection Multi-modal Multi-modal analyze Multi-modal approach Multimodal analysis Systematic literature review Cyber attacks As cyber threats continue to evolve in complexity, traditional malware detection methods often fall short in identifying sophisticated attacks. Multimodal analysis, which integrates various data sources and analytical methods, has emerged as a promising approach to enhance malware detection capabilities. This paper presents a systematic literature review (SLR) of existing research on multimodal analysis techniques for malware detection. We conducted an extensive search across two major databases and identified 31 studies that explore the multimodal approaches for malware detection. Our review synthesizes the methodologies, modalities, and evaluation metrics used in these studies, highlighting their strengths and limitations. The findings reveal that multimodal approaches significantly improve detection accuracy and robustness compared to single-modality methods. However, challenges such as data integration complexity, computational overhead, scalability, and transparency remain. This review also identifies research gaps and suggests directions for future work to further advance the field of malware detection. ? 2024 IEEE. Final 2025-03-03T07:45:02Z 2025-03-03T07:45:02Z 2024 Conference paper 10.1109/ICSSA62312.2024.10788667 2-s2.0-85216511165 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216511165&doi=10.1109%2fICSSA62312.2024.10788667&partnerID=40&md5=a04b8c4970e08c0db88ec5025e924a50 https://irepository.uniten.edu.my/handle/123456789/36835 Institute of Electrical and Electronics Engineers Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Analysis techniques Cyber security Cyber threats Detection methods Malware detection Multi-modal Multi-modal analyze Multi-modal approach Multimodal analysis Systematic literature review Cyber attacks
spellingShingle	Analysis techniques Cyber security Cyber threats Detection methods Malware detection Multi-modal Multi-modal analyze Multi-modal approach Multimodal analysis Systematic literature review Cyber attacks Ibrahim Z.-A. Ismail S.A. Rahim F.A. A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection
description	As cyber threats continue to evolve in complexity, traditional malware detection methods often fall short in identifying sophisticated attacks. Multimodal analysis, which integrates various data sources and analytical methods, has emerged as a promising approach to enhance malware detection capabilities. This paper presents a systematic literature review (SLR) of existing research on multimodal analysis techniques for malware detection. We conducted an extensive search across two major databases and identified 31 studies that explore the multimodal approaches for malware detection. Our review synthesizes the methodologies, modalities, and evaluation metrics used in these studies, highlighting their strengths and limitations. The findings reveal that multimodal approaches significantly improve detection accuracy and robustness compared to single-modality methods. However, challenges such as data integration complexity, computational overhead, scalability, and transparency remain. This review also identifies research gaps and suggests directions for future work to further advance the field of malware detection. ? 2024 IEEE.
author2	57203863738
author_facet	57203863738 Ibrahim Z.-A. Ismail S.A. Rahim F.A.
format	Conference paper
author	Ibrahim Z.-A. Ismail S.A. Rahim F.A.
author_sort	Ibrahim Z.-A.
title	A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection
title_short	A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection
title_full	A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection
title_fullStr	A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection
title_full_unstemmed	A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection
title_sort	systematic literature review of multimodal analysis techniques for malware detection
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2025
_version_	1825816285936615424
score	13.244413

A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection

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