Real-time DDoS detection in software-defined networks using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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| Формат: | Диссертация |
| Язык: | English |
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Brac University
2024
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| Online-ссылка: | http://hdl.handle.net/10361/24344 |
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10361-243442024-10-17T21:03:57Z Real-time DDoS detection in software-defined networks using machine learning Hasan, Kadir Hossain, Kaji Sajjad Apurbo, GM Mohaiminuzzaman Islam, MD Zubairul Alam, Md Shakibul Hossain, Muhammad Iqbal Ahmed, Md Faisal Mukta, Jannatun Noor Department of Computer Science and Engineering, Brac University Distributed denial of service DDoS attacks SDN Machine learning CICIoT2023 Cyber threats Intrusion detection systems (Computer security). Real-time data processing. Denial of service attacks. Computer networks--Security measures. 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 49-52). As the landscape of the digital world keeps changing and getting more advanced, so do the sophistication and complexities of cyber threats. Distributed Denial of Service (DDoS) attacks have become a major threat to network security. Additionally, in software defined networks (SDN), the structure uses a controller to track down the network flow. In this research, we worked with a traditional static dataset, “CICIoT2023” in order to detect DDoS attacks on IoT devices with an efficient approach by applying effective feature engineering using Random Forest and PCA, followed by comparing various machine learning models including Random Forest, KNN, Decision Tree (DT), Logistic Regression (LR) and Naive Bayes. Using only 3 key features out of 47, the research shows that Random Forest selection method gives better accuracy for most of the ML models. Among those ML models, Decision Tree shows 99.97% accuracy with optimal model complexity. Our study also focused on constructing a network topology using Mininet simulation tool and Ryu controller in a SDN environment, which further complies with DDoS detection in real-time networks. Therefore, our research is not only focusing on the efficiency of the traditional approach but also on generating real-time networks to detect DDoS attacks simultaneously. Kadir Hasan Kaji Sajjad Hossain GM Mohaiminuzzaman Apurbo MD Zubairul Islam Md Shakibul Alam B.Sc. in Computer Science 2024-10-17T07:55:09Z 2024-10-17T07:55:09Z ©2024 2024-05 Thesis ID 20101332 ID 20101321 ID 20301100 ID 20101322 ID 20301286 http://hdl.handle.net/10361/24344 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. 65 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Distributed denial of service DDoS attacks SDN Machine learning CICIoT2023 Cyber threats Intrusion detection systems (Computer security). Real-time data processing. Denial of service attacks. Computer networks--Security measures. |
| spellingShingle |
Distributed denial of service DDoS attacks SDN Machine learning CICIoT2023 Cyber threats Intrusion detection systems (Computer security). Real-time data processing. Denial of service attacks. Computer networks--Security measures. Hasan, Kadir Hossain, Kaji Sajjad Apurbo, GM Mohaiminuzzaman Islam, MD Zubairul Alam, Md Shakibul Real-time DDoS detection in software-defined networks using machine learning |
| 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 Hasan, Kadir Hossain, Kaji Sajjad Apurbo, GM Mohaiminuzzaman Islam, MD Zubairul Alam, Md Shakibul |
| format |
Thesis |
| author |
Hasan, Kadir Hossain, Kaji Sajjad Apurbo, GM Mohaiminuzzaman Islam, MD Zubairul Alam, Md Shakibul |
| author_sort |
Hasan, Kadir |
| title |
Real-time DDoS detection in software-defined networks using machine learning |
| title_short |
Real-time DDoS detection in software-defined networks using machine learning |
| title_full |
Real-time DDoS detection in software-defined networks using machine learning |
| title_fullStr |
Real-time DDoS detection in software-defined networks using machine learning |
| title_full_unstemmed |
Real-time DDoS detection in software-defined networks using machine learning |
| title_sort |
real-time ddos detection in software-defined networks using machine learning |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24344 |
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