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@article{170702,
        author = {Pranav Hirulkar and Prof. Rahul Suryawanshi and Ritesh Badguye and Prathmesh Masalkar and Rushikesh Samudrawar},
        title = {DDoS Attack Detection using SDN Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1863-1869},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170702},
        abstract = {Software Defined Networking (SDN) has recently gained significant attention in research due to its innovative design, which separates the control plane from the data plane. This separation allows for easier and more efficient network programmability compared to traditional networks. In conventional networks, any change would require reconfiguration of multiple resources, whereas in SDN, a single individual with expertise in the control plane (controller) can manage all network resources and update rules in a much shorter time. One of the most prevalent and damaging attacks today is the Distributed Denial of Service (DDoS) attack, which renders services unavailable for an extended period. In this paper, we propose a method to detect DDoS attacks targeting one or multiple victims simultaneously by combining two machine learning algorithms: entropy and Principal Component Analysis (PCA). We evaluate the effectiveness of our approach using the Mininet emulator, a POX controller, and Open vSwitch as the switch. Our results demonstrate a high detection accuracy in identifying DDoS attacks.},
        keywords = {SDN, DDoS attack, Controller, PCA, Entropy, Machine Learning.},
        month = {December},
        }