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.

Detalles Bibliográficos
Autores principales:	Rahman, Tahsinur, Ahmed, Nusaiba, Monjur, Shama, Haque, Fasbeer Mohammad, Kabir, Naweed
Otros Autores:	Hossain, Dr. Muhammad Iqbal
Formato:	Tesis
Lenguaje:	English
Publicado:	Brac University 2023
Materias:	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.
Acceso en línea:	http://hdl.handle.net/10361/19354

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

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