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Singh et al. [7] assert that serious flaws in widely used document readers, such as Adobe Acrobat Reader, are still being discovered. Ultimately, it is well known that most detection techniques currently in use rely on signatures or heuristics, which are considered less efficient and perform
poorly against new vulnerabilities [9], [10]. As attackers continue to refine their methods and make them more complex, efficient, and proactive solutions to protect PDF files against these evolving threats and breaches are necessary.
Liu et al. [15] suggested that the effectiveness of ML solutions in such contexts largely depend on the quality of the predictors and the dataset used. In this light, this study proposes to leverage ML to introduce a novel, efficient, and informative solution against PDF malware.
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IEEE/ICACT20260055 Slide.03
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