Machine Learning-Based DDoS Saturation Attack Detection and analysis in SDN Environment
P Sandeep Kumar Reddy, M SriRaghavendra, K Sreenivasulu, T N Balakrishna,
Affiliations Department of CSE, G.Pullaiah College of Engineering and Technology, Kurnool
To create dynamic, adaptable, manageable, and cost-effective computer networks, Software Defined Network (SDN) has been developed as a new methodology. As a result, the security of SDN is essential. Switches in SDN can match incoming packets to flow tables but not process anything. To identify SDN, DDoS, and saturation attacks, different Machine Learning-based detection methods have recently been presented. This method detects and analyses DDoS saturation attacks using Machine Learning in an SDN environment. The presented model utilizes a variety of Machine Learning (ML) methods, including AdaBoost, Decision Tree (DT), and Support Vector Machine (SVM). Experimental results clearly express that the described Machine Learning model provides more Accuracy, Precision, Recall and F1-Score compared to simple Machine Learning models. The combined Machine Learning (SVM+ DT+ AdaBoost) accuracy is 97.6%, precision, recall, F1-score values are 96.6%, 97.4%, 98% respectively.
P Sandeep Kumar Reddy,MSriRaghavendra,K Sreenivasulu,T N Balakrishna."Machine Learning-Based DDoS Saturation Attack Detection and analysis in SDN Environment". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.9, Issue 12,pp.269-274, December- 2022, URL :https://ijcert.org/ems/ijcert_papers/V9I1206.pdf,
Keywords : Software Defined Network (SDN), Distributed Denial of Service Attacks (DDoS), Machine Learning, SVM, DT and AdaBoost.
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