MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí:	Haque, Abid Hossain, Jahin, Labiba Ifrit, Katib, Sheikh Yasir Hossain, Tuhee, Saiwara Mahmud, Tasnia, Maisoon
Rannpháirtithe:	Hossain, Muhammad Iqbal
Formáid:	Tráchtas
Teanga:	English
Foilsithe / Cruthaithe:	Brac University 2024
Ábhair:	Explainable artificial intelligence (XAI) Convolutional neural network (CNN) DenseNet Inception ResNet V2 VGG16 Comparative analysis Neural networks (Computer science) Artificial intelligence
Rochtain ar líne:	http://hdl.handle.net/10361/22824

id	10361-22824
record_format	dspace
spelling	10361-228242024-05-15T21:04:14Z MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI Haque, Abid Hossain Jahin, Labiba Ifrit Katib, Sheikh Yasir Hossain Tuhee, Saiwara Mahmud Tasnia, Maisoon Hossain, Muhammad Iqbal Ahmed, Md Faisal Department of Computer Science and Engineering, Brac University Explainable artificial intelligence (XAI) Convolutional neural network (CNN) DenseNet Inception ResNet V2 VGG16 Comparative analysis Neural networks (Computer science) Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 73-76). Just as the digital transformation of everything in this ‘Information Age’ has acted substantially to mitigate conventional crimes to a degree, the rate of cyber crime has parallelly elevated alarmingly. As malware has been the primary envoy in such criminal incidents, its metamorphosis is highly prevalent. This paper presents a systematic grouping of malware samples into distinct families extracted from two prominent datasets, MalImg and MaleVis through extensive research. Subsequently, six state-of-the-art advanced CNN architectures have been utilized including Inception ResNet V2, DenseNet, VGG16, ResNet50, EfficientNetB0 and XceptionNet. Then a comprehensive analysis of malware classification was conducted as the research aimed to discern the performance variances among these models concerning the classification of diverse malware families. Moreover, eXplainable Artificial Intelligence (XAI) techniques, particularly Local Interpretable Model-agnostic Explanations (LIME) has been introduced, to deduce the rationale behind the classification decisions made by each model. This involved analyzing and visualizing the salient features within the malware files that led to their identification as malicious entities. Lastly, the findings of this study not only provide a comparative evaluation of various deep learning architectures for malware classification but also offer insightful explanations through XAI methodologies, shedding light on the interpretability of model decisions in the realm of cybersecurity. The results furnish valuable insights for enhancing the understanding of malware behaviour and model interpretability, thereby contributing to the advancement of robust and explainable malware detection systems. Abid Hossain Haque Labiba Ifrit Jahin Sheikh Yasir Hossain Katib Saiwara Mahmud Tuhee Maisoon Tasnia B.Sc. in Computer Science 2024-05-15T03:26:40Z 2024-05-15T03:26:40Z ©2024 2024-01 Thesis ID: 20101453 ID: 20101467 ID: 20301013 ID: 20101465 ID: 20301076 http://hdl.handle.net/10361/22824 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. 77 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Explainable artificial intelligence (XAI) Convolutional neural network (CNN) DenseNet Inception ResNet V2 VGG16 Comparative analysis Neural networks (Computer science) Artificial intelligence
spellingShingle	Explainable artificial intelligence (XAI) Convolutional neural network (CNN) DenseNet Inception ResNet V2 VGG16 Comparative analysis Neural networks (Computer science) Artificial intelligence Haque, Abid Hossain Jahin, Labiba Ifrit Katib, Sheikh Yasir Hossain Tuhee, Saiwara Mahmud Tasnia, Maisoon MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Haque, Abid Hossain Jahin, Labiba Ifrit Katib, Sheikh Yasir Hossain Tuhee, Saiwara Mahmud Tasnia, Maisoon
format	Thesis
author	Haque, Abid Hossain Jahin, Labiba Ifrit Katib, Sheikh Yasir Hossain Tuhee, Saiwara Mahmud Tasnia, Maisoon
author_sort	Haque, Abid Hossain
title	MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI
title_short	MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI
title_full	MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI
title_fullStr	MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI
title_full_unstemmed	MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI
title_sort	malfam: a comprehensive study on malware families with state-of-the-art cnn architectures with classifications and xai
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22824
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MalFam: a comprehensive study on malware families with state-of-the-art CNN architectures with classifications and XAI

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