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.
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Brac University
2023
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10361-193542023-08-08T21:02:03Z PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework Rahman, Tahsinur Ahmed, Nusaiba Monjur, Shama Haque, Fasbeer Mohammad Kabir, Naweed Hossain, Dr. Muhammad Iqbal Department of Computer Science and Engineering, Brac University 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. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 47-49). As the world is moving more and more towards a digital era, a great majority of data is transferred through a famous format known as PDF. One of its biggest obstacles is still the age-old problem: malware. Even though several anti-malware and anti-virus software exist, many of which cannot detect PDF Malware. Emails carrying harmful attachments have recently been used in targeted cyber attacks against businesses. Because most email servers do not allow executable files to be attached to emails, attackers prefer to use non-executable files like PDF files. In various sectors, machine learning algorithms and neural networks have been proven to successfully detect known and unidentified malware. However, it can be difficult to understand how these models make their decisions. Such lack of transparency can be a problem, as it is important to understand how an AI system is making decisions in order to ensure that it is acting ethically and responsibly. In some cases, machine and deep learning models may make biased or discriminatory decisions or have unintended consequences. Hence, Explainable AI comes into play. To address this issue, this paper suggests using machine learning algorithms SGD(Stochastic Gradient Descent), XGBoost Classifier, and deep learning algorithms Single Layer Perceptron, ANN(Artificial Neural Network) and check their interpretability using Explainable AI (XAI)’s SHAP framework to classify a PDF file being malicious or clean for a global and local understanding of the models. Tahsinur Rahman Nusaiba Ahmed Shama Monjur Fasbeer Mohammad Haque Naweed Kabir B. Computer Science and Engineering 2023-08-08T05:32:47Z 2023-08-08T05:32:47Z 2023 2023-01 Thesis ID: 19101146 ID: 19101236 ID: 18201125 ID: 19101269 ID: 19101053 http://hdl.handle.net/10361/19354 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 49 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Malware 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. |
| spellingShingle |
Malware 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. Rahman, Tahsinur Ahmed, Nusaiba Monjur, Shama Haque, Fasbeer Mohammad Kabir, Naweed PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Hossain, Dr. Muhammad Iqbal |
| author_facet |
Hossain, Dr. Muhammad Iqbal Rahman, Tahsinur Ahmed, Nusaiba Monjur, Shama Haque, Fasbeer Mohammad Kabir, Naweed |
| format |
Thesis |
| author |
Rahman, Tahsinur Ahmed, Nusaiba Monjur, Shama Haque, Fasbeer Mohammad Kabir, Naweed |
| author_sort |
Rahman, Tahsinur |
| title |
PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework |
| title_short |
PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework |
| title_full |
PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework |
| title_fullStr |
PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework |
| title_full_unstemmed |
PDFGuardian: An innovative approach to interpretable PDF malware detection using XAI with SHAP framework |
| title_sort |
pdfguardian: an innovative approach to interpretable pdf malware detection using xai with shap framework |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/19354 |
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