DDoS Attack Detection in Software-Defined Networks Using ML/DL

Software-Defined Networking (SDN) has revolutionized network management by centralizing control and enabling dynamic, programmable network configurations. However, SDN environments are not immune to Distributed Denial of Service (DDoS) attacks, which pose a serious threat to network availability and performance. This thesis proposes an approach to enhancing DDoS attack detection and mitigation in SDN environments by ML/DL security solutions.

The methodology begins with a comprehensive literature review encompassing SDN architecture, DDoS attack characteristics, and ML/DL-based security applications. Following this, analysis of DDoS attack datasets and SDN traffic traces is conducted to discern patterns in malicious traffic behavior. A ML/DL-based framework is then designed and implemented to generate synthetic traffic data for training DDoS detection models within an SDN environment. Finally, simulation using network emulation tools like Mininet and SDN testbeds are performed to evaluate the proposed solution's performance and efficacy.

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