Performance analysis of machine learning classi ers for detecting PE malware
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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| Langue: | English |
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Brac University
2020
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| Accès en ligne: | http://hdl.handle.net/10361/13837 |
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10361-13837 |
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10361-138372022-01-26T10:04:56Z Performance analysis of machine learning classi ers for detecting PE malware Azmee, ABM.Adnan Choudhury, Pranto Protim Alam, Md.Aosaful Dutta, Orko Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Malware detection Machine learning Data protection XGBoost Support Vector Machine Extra Tree Classi er Client- Server Model Neural networks Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 58-60). In this modern era of technology, securing and protecting one's data has been a major concern and needs to be focused on. Malware is a program that is designed to cause harm and malware analysis is one of the paramount focused points under the sight of cyber forensic professionals and network administrations. The degree of the harm brought about by malignant programming varies to a great extent. If this happens at home to a random person then that may lead to some loss of irrel- evant or unimportant information but for a corporate network, it can lead to loss of valuable business data. The existing research does focus on some few machine learning algorithms to detect malware and very few of them worked with Portable Executables (PE) les. However, we worked on the PE les and also for real-time computation, a client-server model was developed by using Flask to detect malware or benign. In this paper, we mainly focused on top classi cation algorithms and compare their accuracy to nd out which one is giving the best result according to the dataset and also compare among these algorithms. Top machine learning clas- si cation algorithms were used alongside neural networks such as Arti cial Neural Network, XGBoost, Support Vector Machine, Extra Tree Classi er, etc. The exper- imental result shows that XGBoost achieved the highest accuracy of 98.62 percent when compared with other approaches. Thus, to provide a better solution for this kind of anomalies, we have been interested in researching malware detection and want to contribute to building strong and protective cybersecurity. ABM. Adnan Azmee Md. Aosaful Alam Pranto Protim Choudhury Orko Dutta B. Computer Science 2020-03-08T06:56:30Z 2020-03-08T06:56:30Z 2019 2019-12 Thesis ID 16101155 ID 16101062 ID 16101061 ID 16101022 http://hdl.handle.net/10361/13837 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. 60 pages 60 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Malware detection Machine learning Data protection XGBoost Support Vector Machine Extra Tree Classi er Client- Server Model Neural networks Machine learning |
| spellingShingle |
Malware detection Machine learning Data protection XGBoost Support Vector Machine Extra Tree Classi er Client- Server Model Neural networks Machine learning Azmee, ABM.Adnan Choudhury, Pranto Protim Alam, Md.Aosaful Dutta, Orko Performance analysis of machine learning classi ers for detecting PE malware |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Azmee, ABM.Adnan Choudhury, Pranto Protim Alam, Md.Aosaful Dutta, Orko |
| format |
Thesis |
| author |
Azmee, ABM.Adnan Choudhury, Pranto Protim Alam, Md.Aosaful Dutta, Orko |
| author_sort |
Azmee, ABM.Adnan |
| title |
Performance analysis of machine learning classi ers for detecting PE malware |
| title_short |
Performance analysis of machine learning classi ers for detecting PE malware |
| title_full |
Performance analysis of machine learning classi ers for detecting PE malware |
| title_fullStr |
Performance analysis of machine learning classi ers for detecting PE malware |
| title_full_unstemmed |
Performance analysis of machine learning classi ers for detecting PE malware |
| title_sort |
performance analysis of machine learning classi ers for detecting pe malware |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/13837 |
| work_keys_str_mv |
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| _version_ |
1814307059759316992 |