Development of Risk Assessment and Minimization Models for Cyber Incidents in Interconnected Automotive and Power Systems
TL;DR
A systematic risk minimization framework that integrates proactive and reactive measures: from the deployment of specialized intrusion detection systems (IDS) optimized for industrial control protocols to the implementation of adaptive load management algorithms that mitigate the effects of malicious demand-side manipulation.
Abstract
The paper investigates the critical problem of ensuring cyber resilience within the integrated ecosystems of automotive transport and energy infrastructure, which are becoming increasingly interdependent due to the mass adoption of electric vehicles and Smart Grid technologies. The study provides a comprehensive analysis of the threat landscape, focusing on specific attack vectors targeting electric vehicle charging stations (EVCS) and the communication protocols of the Vehicle-to-Grid (V2G) interface. It is demonstrated that vulnerabilities in the ISO/IEC 15118 and OCPP protocols can be exploited to initiate cascading failures that transcend the boundaries of the transport network and impact the stability of the regional power grid. The central contribution of this research is the development of a formalized mathematical model for multi-layer risk assessment, which utilizes a probabilistic approach to quantify the impact of cyber-physical attacks on system availability and data integrity. Unlike existing one-dimensional models, the proposed methodology accounts for the interconnectedness of nodes, where a security breach in a single vehicle or charging point acts as a catalyst for large-scale energy imbalances. The paper details a systematic risk minimization framework that integrates proactive and reactive measures: from the deployment of specialized intrusion detection systems (IDS) optimized for industrial control protocols to the implementation of adaptive load management algorithms that mitigate the effects of malicious demand-side manipulation. Simulation results presented in the study confirm that the proposed model effectively identifies high-risk convergence points with a sensitivity improvement of 15-20% compared to traditional NIST-based frameworks. The research findings provide a theoretical and practical basis for government agencies and critical infrastructure operators to develop robust cybersecurity strategies in the era of total digitalization of transport and energy assets. The developed models contribute to the creation of autonomous defense mechanisms capable of maintaining operational continuity under adversarial conditions.