CPADP: Novel Processor for Efficient Threat Detection in CPS using RISC and DC-GCNN

In the realm of cybersecurity for cyber-physical systems (CPS), the need for robust and efficient intrusion detection mechanisms has become increasingly critical. The existing CPS had limited processing capabilities of traditional detection systems in CPS, and incompatibility of existing IT- centric security solutions with CPS environments. The challenges in real-time threat detection and response due to high data volumes. Vulnerabilities arising from interconnected nature of cyber-physical systems. This paper proposes a novel Cyber-Physical Attack Detection Processor (CPADP) based on Reduced Instruction Set Computing (RISC) architecture. The CPADP integrates advanced techniques for real-time threat detection in CPS environments, leveraging a comprehensive approach spanning data collection, preprocessing, Independent Component Analysis (ICA)-based feature extraction, and a Dual Channel Graph Convolutional Neural Network (DC-GCNN) for attack classification. The architecture aims to address the challenges posed by CPS environments, where traditional IT- centric security measures often fall short. By harnessing the capabilities of RISC architecture, the CPADP promises enhanced processing efficiency and scalability, crucial for real-time threat mitigation in diverse CPS applications.