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Torres and De Los Santos [33] developed a privacy-enforced framework based on machine learning for detecting malicious PDF documents, with a focus on those containing embedded JavaScript code. The dataset consisted of 1,712 PDF samples collected from various sources. The table demonstrates that the proposed hybrid solution outperformed theirs across all metrics, including the ROC (0.98).
Chen et al. [13] employed a novel distance metric for PDF tree structures and state-of-the-art verifiably robust training methods. However, Chen et al. [13] only addressed simple robustness properties and ignored more complex ones that could have led to stronger security guarantees. Additionally, their most optimal solution, Robust A+B, is seen to have performed negligibly better than the proposed one, despite using a few metrics for its evaluation.
Falah et al. [6] enhanced feature engineering and data-driven mechanisms to combine features from PDFiD and PeePDF analysis tools, along with derived complementary features. With samples from VirusTotal, they evaluated their approach, with RF standing as the most efficient. However, the proposed CNN+ViT architecture steadily outperformed it. The same can be said for the works of Abu Al-Haija et al. [16] and Alshamrani [12], even though Alshamrani [12] barely had enough metrics to compare with. The dataset employed by Abu Al-Haija et al. [13] is relatively older, thus providing the proposed solution with an additional edge.
Hossain et al. [34] developed a dataset comprising 15,958 PDF samples from which features were extracted for training ML algorithms. The random forest(RF) classifier performed very well on the combined feature set, as seen in the table. However, because it was trained and assessed using a larger and more recent dataset as well as a greater variety of metrics, the suggested approach is objectively superior. Yet the proposed study has led to the discovery of even fewer sets of features that can yield even better results across the necessary metrics.
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IEEE/ICACT20260055 Slide.16
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