An Intelligent Cyber-Physical Security Framework for Integrated Railway and Smart Port Operations

Abstract

 With the fast pace of digitalization of transport infrastructure, smart elements and technologies have been integrated into railway networks and smart port operations, improving the degree of automation, real-time monitoring, predictive maintenance, and efficient logistics management. While all these developments enhance the efficiency and reliability of operations, they create new cybersecurity challenges with the increasing interconnections between cyber and physical elements. Such cyber threats as malware, distributed denial-of-service (DDoS) attacks, GPS spoofing, insider attacks, false data injection, and sensor tampering can impact transportation operations, tamper with operational data, and pose a threat to public safety for critical infrastructures. This research introduces an intelligent cyber-physical security framework for integrated railway and smart port operations, which integrates artificial intelligence (AI), Internet of Things (IoT) technologies, edge computing, blockchain-based secure communication, modeling and simulation with a digital twin, and decision support functions to create an all-encompassing cybersecurity architecture for the integrated transportation systems. The proposed framework was able to continuously retrieve diverse operational data from the railway signaling systems, smart port automation equipment, industrial control systems, IoT sensors, surveillance systems, communication networks, and logistics databases for real-time security analysis. Intelligent threat detection and classification are achieved with the help of advanced AI algorithms, and blockchain technology offers secure authentication and trusted information sharing and tamper-resistant data management. Edge computing can minimize communication delays by analyzing security data near operational assets, while digital twin technology can be used for predictive security risk evaluation, cyberattack simulation, infrastructure monitoring, and assessing infrastructure resilience. The experimental evaluation of the performance shows the ability to detect intrusions with better accuracy, false alarm rate, throughput, response time, and AUC values than the traditional machine learning and deep learning models.