A study of malware classification using deep learning
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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| Jazyk: | English |
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2024
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| On-line přístup: | http://hdl.handle.net/10361/23557 |
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10361-235572024-06-25T21:02:11Z A study of malware classification using deep learning Rahman, Mohammad Muhibur Ahmed, Anushua Khan, Mutasim Husain Jamshed, Abrar Rahman, Md Hafijur Kaykobad, Mohammad Department of Computer Science and Engineering, Brac University Malware Deep learning Neural network Transformer Data mining Malware (Computer software)--Prevention Electronic transformers--Design and construction 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 46-50). Malware represents an intrusive computer program that is engineered by cybercriminals to destroy computer systems or steal and manipulate sensitive data. Malware classification is crucial to malware detection as it helps to assign malware to a specific category according to its characteristics. Characterizing and labeling variants of spyware is also useful as it will shed light on how they’re able to gain access to our systems in the first place, the dangers they possess, and the necessary preventions to take against them. In order to tackle such a serious security-related issue, we have decided to develop an image-processing system that would help us be faster at detecting malware while also possibly being one step ahead of cybercriminals. To describe and categorize sourced malware datasets, we will develop the system using various approaches for deep learning methods and even propose a simple CNN-based methodology of our own. The aim of our work is to show a comparative study of malware types with experimental results, making it easier to identify and keep track of malware that already exists while helping to detect new ones. To be more specific, we worked with four pre-trained CNN models in order to diversify our methods. These trained models include ResNet-50, Inception-V3, VGG-16, and DenseNet-201. After running and testing all of the models on the Malimg dataset, our suggested model was able to achieve a 97.64% accuracy rate in detecting malware greyscale images. This high level of testing accuracy also slightly outperformed some of the other cutting-edge models used in our comparison study on the dataset. These modern and highly developed models used for comparison include Involution, Vision Transformer (ViT), Compact Convolutional Transformer (CCT), and External Attention Network (EANet). Finally, we employed the use of an explainable artificial intelligence (AI) technique known as LIME to provide a more detailed clarification of the rationale behind our model’s selection and classification of individual samples into their respective classes. Mohammad Muhibur Rahman Anushua Ahmed Mutasim Husain Khan Abrar Jamshed Md Hafijur Rahman B.Sc in Computer Science 2024-06-25T04:05:16Z 2024-06-25T04:05:16Z ©2023 2023-09 Thesis ID 19201079 ID 19201082 ID 19201002 ID 19301058 ID 19201067 http://hdl.handle.net/10361/23557 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. application/pdf |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Malware Deep learning Neural network Transformer Data mining Malware (Computer software)--Prevention Electronic transformers--Design and construction |
| spellingShingle |
Malware Deep learning Neural network Transformer Data mining Malware (Computer software)--Prevention Electronic transformers--Design and construction Rahman, Mohammad Muhibur Ahmed, Anushua Khan, Mutasim Husain Jamshed, Abrar Rahman, Md Hafijur A study of malware classification using deep learning |
| 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 |
Kaykobad, Mohammad |
| author_facet |
Kaykobad, Mohammad Rahman, Mohammad Muhibur Ahmed, Anushua Khan, Mutasim Husain Jamshed, Abrar Rahman, Md Hafijur |
| format |
Thesis |
| author |
Rahman, Mohammad Muhibur Ahmed, Anushua Khan, Mutasim Husain Jamshed, Abrar Rahman, Md Hafijur |
| author_sort |
Rahman, Mohammad Muhibur |
| title |
A study of malware classification using deep learning |
| title_short |
A study of malware classification using deep learning |
| title_full |
A study of malware classification using deep learning |
| title_fullStr |
A study of malware classification using deep learning |
| title_full_unstemmed |
A study of malware classification using deep learning |
| title_sort |
study of malware classification using deep learning |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23557 |
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