| Title | Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Sudar, K.Muthamil, Beulah, M., Deepalakshmi, P., Nagaraj, P., Chinnasamy, P. |
| Conference Name | 2021 International Conference on Computer Communication and Informatics (ICCCI) |
| Keywords | attack vectors, composability, Decision Tree, Decision trees, denial-of-service attack, Distributed Denial of Service (DDoS), Human Behavior, machine learning, machine learning algorithms, Predictive Metrics, pubcrawl, Resiliency, Scalability, SDN security, security, Software, Software algorithms, software-defined network (SDN), support vector machine (SVM), Support vector machines |
| Abstract | Software-defined network (SDN) is a network architecture that used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it's not possible to change dynamically, because it's a fixed connection. SDN is a good approach but still is vulnerable to DDoS attacks. The DDoS attack is menacing to the internet. To prevent the DDoS attack, the machine learning algorithm can be used. The DDoS attack is the multiple collaborated systems that are used to target the particular server at the same time. In SDN control layer is in the center that link with the application and infrastructure layer, where the devices in the infrastructure layer controlled by the software. In this paper, we propose a machine learning technique namely Decision Tree and Support Vector Machine (SVM) to detect malicious traffic. Our test outcome shows that the Decision Tree and Support Vector Machine (SVM) algorithm provides better accuracy and detection rate. |
| DOI | 10.1109/ICCCI50826.2021.9402517 |
| Citation Key | sudar_detection_2021 |
Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques