Towards Lightweight Intrusion Identification in SDN-based Industrial Cyber-Physical Systems

Abstract

Software-defined networks (SDN)-based industrial cyber-physical systems (CPS) enable customizing development opportunities with integrated network interconnection to perform monitoring, measurement, control system, and security tasks. The extensive connectivity and the vast amount of data exchange in the SDN-based industrial CPS environment make it vulnerable to cyberattacks. Furthermore, an SDN controller is a single attractive target for an attack. It is challenging when the SDN controller manages DL-based high-complexity intrusion detection in an IIoT network with low latency requirements to identify and prevent attacks. This study proposes a lightweight intrusion detection model in an SDN-based industrial CPS environment. The proposed model was evaluated using a recent publicly SDNrelated cyber-security InSDN dataset. The experimental results show that the proposed model outperforms the state-of-the-art by achieving 98.95% accuracy, 99.00% precision, 98.91% recall, and a 0.164 ms time cost when using the LightGBM feature selection technique.