Biblio

The papers in this special section focus on resilient control in large-scae networked cyber-physical systems. These papers deal with the opportunities offered by these emerging technologies to mitigate undesired phenomena arising when intentional jamming and false data injections, categorized as cyber-attacks, infer communication channels. Recent advances in sensing, communication and computing have open the door to the deployment of largescale networks of sensors and actuators that allow fine-grain monitoring and control of a multitude of physical processes and infrastructures. The appellation used by field experts for these paradigms is Cyber-Physical Systems (CPS) because the dynamics among computers, networking media/resources and physical systems interact in a way that multi-disciplinary technologies (embedded systems, computers, communications and controls) are required to accomplish prescribed missions. Moreover, they are expected to play a significant role in the design and development of future engineering applications such as smart grids, transportation systems, nuclear plants and smart factories.

With the continuous emergence of cyber attacks, the security of industrial control system (ICS) has become a hot issue in academia and industry. Intrusion detection technology plays an irreplaceable role in protecting industrial system from attacks. However, the imbalance between normal samples and attack samples seriously affects the performance of intrusion detection algorithms. This paper proposes SE-IDS, which uses generative adversarial networks (GAN) to expand the minority to make the number of normal samples and attack samples relatively balanced, adopts particle swarm optimization (PSO) to optimize the parameters of LightGBM. Finally, we evaluated the performance of the proposed model on the industrial network dataset.