PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Dettagli Bibliografici
Autori principali:	Rahman, Tahsinur, Ahmed, Nusaiba, Monjur, Shama, Haque, Fasbeer Mohammad, Kabir, Naweed
Altri autori:	Hossain, Dr. Muhammad Iqbal
Natura:	Tesi
Lingua:	English
Pubblicazione:	Brac University 2023
Soggetti:	Malware PDF PDF-analysis Cybersecurity SGD Machine-learning Detection Deep learning Artificial neural network Algorithm Single layer perceptron Extreme gradient boosting Explainable artificial intelligence Shapley additive explanations ANN SHAP XAI XGBoost Classifiers Artificial intelligence. Computer security.
Accesso online:	http://hdl.handle.net/10361/19354

http://hdl.handle.net/10361/19354

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