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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| Format: | Conference paper |
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Institute of Electrical and Electronics Engineers Inc.
2025
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| Summary: | 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. |
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