A machine learning based approach for DDos attack detection
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
2024
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| 在线阅读: | http://hdl.handle.net/10361/23575 |
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10361-235752024-06-25T21:01:18Z A machine learning based approach for DDos attack detection Tahrim, Tasmiah Sharan, MD Asif Monsur, Meshaq Hasan, MD Abid Azad, Tanzina Binte Shakil, Arif Ahmed, Md Faisal Department of Computer Science and Engineering, Brac University DDoS attack Cyber-security Machine learning Random forest classifier HTTP based protocol Transport layer Application layer Computer security Machine learning HTTP (Computer network protocol) 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 44-45). The modern era saw the rise of technologies in almost every sector. Computers are gradually becoming faster and smaller, also allowing people to utilize them almost everywhere. As computer technology has become an important part to simplify our life at work, the security of computer networks is one of the hardest challenges for the technology experts to overcome. Network security is a must because it protects private information from online attacks and upholds the dependability of the network. In this study, after reviewing a few previous papers and research works, we decided to work on the detection process of DDoS that can be used on the web or server security. Working on the datasets (CICDDoS2019) to merge them and create a new taxonomy for detecting DDoS attacks was our primary step. Then, the data were generated for the two types of attack which are Reflection based and Exploitation based to reduce the time consumption. Thirdly, using the generated dataset, some Machine Learning based models and classifiers have been implemented on important features that have the most contributions. For getting a better accuracy rate, Random Forest, Naive Bayes, Decision Tree and XGBoost model were applied. Finally, we get a better accuracy rate with these models to detect the attack in a reduced amount of time. Tasmiah Tahrim MD Asif Sharan Meshaq Monsur MD Abid Hasan Tanzina Binte Azad B.Sc in Computer Science 2024-06-25T06:23:28Z 2024-06-25T06:23:28Z ©2023 ©2023 2023-09 Thesis ID 19101101 ID 18201134 ID 23341133 ID 18101692 ID 20201217 http://hdl.handle.net/10361/23575 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. 57 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
DDoS attack Cyber-security Machine learning Random forest classifier HTTP based protocol Transport layer Application layer Computer security Machine learning HTTP (Computer network protocol) |
| spellingShingle |
DDoS attack Cyber-security Machine learning Random forest classifier HTTP based protocol Transport layer Application layer Computer security Machine learning HTTP (Computer network protocol) Tahrim, Tasmiah Sharan, MD Asif Monsur, Meshaq Hasan, MD Abid Azad, Tanzina Binte A machine learning based approach for DDos attack detection |
| 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 |
Shakil, Arif |
| author_facet |
Shakil, Arif Tahrim, Tasmiah Sharan, MD Asif Monsur, Meshaq Hasan, MD Abid Azad, Tanzina Binte |
| format |
Thesis |
| author |
Tahrim, Tasmiah Sharan, MD Asif Monsur, Meshaq Hasan, MD Abid Azad, Tanzina Binte |
| author_sort |
Tahrim, Tasmiah |
| title |
A machine learning based approach for DDos attack detection |
| title_short |
A machine learning based approach for DDos attack detection |
| title_full |
A machine learning based approach for DDos attack detection |
| title_fullStr |
A machine learning based approach for DDos attack detection |
| title_full_unstemmed |
A machine learning based approach for DDos attack detection |
| title_sort |
machine learning based approach for ddos attack detection |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23575 |
| work_keys_str_mv |
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