La-Casette/malicious_pdf_detection
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.
This project helps cybersecurity analysts automatically identify harmful PDF files that could infect systems. It takes suspicious PDF files and uses machine learning to determine if they are malicious, helping you protect your organization from cyber threats. Security operations center (SOC) analysts or incident responders would find this useful for flagging potentially dangerous documents.
No commits in the last 6 months.
Use this if you need a robust way to automatically screen incoming PDF documents for potential malware threats.
Not ideal if you need to detect malicious files in formats other than PDF, such as executables or office documents.
Stars
8
Forks
1
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jan 22, 2023
Commits (30d)
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