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.

Bibliographische Detailangaben
Hauptverfasser:	Rahman, Tahsinur, Ahmed, Nusaiba, Monjur, Shama, Haque, Fasbeer Mohammad, Kabir, Naweed
Weitere Verfasser:	Hossain, Dr. Muhammad Iqbal
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2023
Schlagworte:	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.
Online Zugang:	http://hdl.handle.net/10361/19354

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

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