Node Behaviour-Aware Secure Flow Control Mechanism for IoT-Based Big Data

The advent of Internet of Things (IoT) has resulted in a massive influx of data from remote networks, with big data originators located at the edge of the Internet. Software-Defined Networking (SDN) has recently emerged as an effective tool for centralized network control, including access nodes, to manage and optimize the flow of data generated by a vast number of IoT devices. However, remote and relay-positioned access nodes are often vulnerable to security attacks. In this paper, we propose DL-IPS, a novel intrusion detection mechanism for Software-Defined IoT (SD-IoT) based on Deep Learning (DL) techniques. Our proposed approach employs a Deep Neural Network (DNN) algorithm to monitor the traffic behaviours generated from IoT devices and predict potential intruders in the network. The proposed mechanism identifies malicious nodes and packets by monitoring incoming traffic behaviours at the local access nodes, thereby providing protection to the switch and flow tables from various attacks. Furthermore, our approach efficiently places flow rules to devices by removing malicious flows, reducing space consumption and overhead, and detecting anomalies effectively.