Malice PDF Plugin
This project compares the performance of K-Nearest Neighbors, Support Vector Machines, and Decision Trees models for detecting malicious PDF files, with an emphasis on optimizing model performance and analyzing evasion techniques. It provides a comprehensive overview of machine learning for malicious PDF detection and potential vulnerabilities.
Repository for the paper "Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files" published in Applied Sciences, MDPI
PDFScalpel is a forensic PDF analysis and CTF toolkit for security researchers, digital forensics analysts, and penetration testers, providing deep insight into PDF structure, encryption, malware, steganography, metadata, revisions, and document authenticity.
A Python-based static analysis tool that inspects PDF internal structure to detect malicious JavaScript, obfuscated streams, embedded payloads, and indicators of compromise using object & stream level parsing inspired by pdfid, pdf-parser, peepdf, and qpdf methodologies.
Malicious PDF Detector by (random forest & GNN(GAE))
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