Abstract: Malware threats targeting PDF and Word documents have become increasingly prevalent, posing significant risks to information security. The review covers signature-based detection, behavior-based analysis, machine-learning approaches, and hybrid models. By examining the strengths and limitations of each technique, this abstract highlights the current state of research and identifies potential avenues for future improvements in malware detection for PDF and Word documents. The current survey serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into combating malware threats in these widely used file formats.

Keywords: PDF files, Office Documents, malware detection, static analysis, dynamic analysis

Works Cited:

Mrs.Priyanka Pati, Mrs.Madhuri Gedam " Detection of Malware in PDF and Office Documents using Ensemble learning ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 11, pp. 50-59, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.121108

PDF | DOI: 10.17148/IJARCCE.2023.121108

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