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

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.

Bibliografische gegevens
Hoofdauteurs:	Rahman, Tahsinur, Ahmed, Nusaiba, Monjur, Shama, Haque, Fasbeer Mohammad, Kabir, Naweed
Andere auteurs:	Hossain, Dr. Muhammad Iqbal
Formaat:	Thesis
Taal:	English
Gepubliceerd in:	Brac University 2023
Onderwerpen:	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 toegang:	http://hdl.handle.net/10361/19354

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