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HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis
[article]
2021
arXiv
pre-print
Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the identity and behavior of PDF malware. State-of-the-art approaches use machine learning (ML) to learn features that characterize PDF malware. However, ML models are often susceptible to evasion attacks, in which an adversary obfuscates the malware code to avoid being detected by an Antivirus. In this paper, we derive a
arXiv:2111.04703v1
fatcat:vekvcukv2nhm5b6nnk4unaqpwy