Mitigate Volumetric DDoS Attack using Machine Learning Algorithm in SDN based IoT Network Environment

Abstract

Software-Defined Networking (SDN) is a recent trend that is combined with Internet of Things (IoT) in wireless network applications. SDN focus entirely on the upper-level network management and IoT enables monitoring the physical activity of the real-time environment via internet network connectivity. The IoT clusters with SDN often undergoes challenges like network security concerns like getting attacked by a Distributed Denial of Service (DDoS). The mitigation of network management issues is carried out by the frequent software update of SDN. On other hand, the security enhancement is needed to all alleviate the mitigation of security attacks in the network. With such motivation, the research uses machine learning based intrusion detection system to mitigate the DDoS attack in SDN-IoT network. The control layer in the SDN is responsible for the prevention of attacks in IoT network using a strong Intrusion Detection System (IDS) framework. The IDS enables a higher-level attack resistance to the DDoS attack as the framework involves feature selection-based classification model. The simulation is conducted to test the efficacy of the model against various levels of DDoS attacks. The results of simulation show that the proposed method achieves better classification of attacks in the network than other methods.