Leveraging Interpretable Tsetlin Machine for PDF Malware Detection

arXiv:2607.09290v2 Announce Type: replace-cross Abstract: In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality. However, these same capabilities have also made PDF files an attractive attack vector for cyberattackers, who embed malicious code within seemingly legitimate documents to compromise target systems. This paper presents a novel interpretable Tsetlin Machine (TM)-based framework for PDF malware detection. The proposed framework extracts salient features from PDF documents through static analysis without executing the files and employs rule-based learning to accurately classify benign and malicious PDF documents. Numerical evaluation on the RIT-PDFMal-2026 dataset demonstrates that the proposed framework achieves an accuracy of 98.02%, outperforming several state-of-the-art machine learning classifiers. Moreover, the proposed framework provides intrinsic interpretability by transparently explaining its classification decisions. Edge deployment on a Raspberry Pi further supports real-time, on-device PDF malware detection. The combination of better accuracy, computational efficiency, and intrinsic interpretability makes the proposed framework a promising solution for practical PDF malware detection.

Abstract:In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality. However, these same capabilities have also made PDF files an attractive attack vector for cyberattackers, who embed malicious code within seemingly legitimate documents to compromise target systems. This paper presents a novel interpretable Tsetlin Machine (TM)-based framework for PDF malware detection. The proposed framework extracts salient features from PDF documents through static analysis without executing the files and employs rule-based learning to accurately classify benign and malicious PDF documents. Numerical evaluation on the RIT-PDFMal-2026 dataset demonstrates that the proposed framework achieves an accuracy of 98.02%, outperforming several state-of-the-art machine learning classifiers. Moreover, the proposed framework provides intrinsic interpretability by transparently explaining its classification decisions. Edge deployment on a Raspberry Pi further supports real-time, on-device PDF malware detection. The combination of better accuracy, computational efficiency, and intrinsic interpretability makes the proposed framework a promising solution for practical PDF malware detection.

8 pages, 17 figures, 7 tables. Submitted to the IEEE Symposium Series on Computational Intelligence, 2027

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2607.09290 [cs.CR]

(or arXiv:2607.09290v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.09290 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

analiza aiqbench

Leveraging Interpretable Tsetlin Machine for PDF Malware Detection

Subiecte conexe