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@article{203421,
        author = {Kogira Sai Teja and Manthan S Kumar and Mahaprabhu L and Nagalakshmi R and Rekha Jayaram},
        title = {Real-Time Intrusion Detection in SDN Environments Using Random Forest and Ryu Controller: A Neptune Attack Mitigation Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11952-11960},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203421},
        abstract = {Software Defined Networking (SDN) separates the control and data planes to enable centralized, programmable network management, but this centralization also makes the controller vulnerable to attacks such as DDoS and SYN flooding. Traditional rule-based detection methods struggle to handle such dynamic threats in real time. This work proposes a machine learning–driven security framework using a customized Ryu controller with Mininet and MiniNAM. The controller monitors traffic, extracts features, classifies flows as normal or malicious, and dynamically installs OpenFlow rules to block attacks at the switch level. Implemented in Python with Ryu, Open switch, and Mininet, the system is tested under varied traffic scenarios using ping, hping3, and iperf. Results show improved adaptability and accuracy over static rule-based approaches, demonstrating the effectiveness of integrating machine learning with SDN for modern network security.},
        keywords = {Software-Defined Networking (SDN); Intrusion Detection System (IDS); Ryu Controller; Mininet; OpenFlow Protocol; Random Forest},
        month = {May},
        }