Building Cyber-Attack Immunity in Electric Energy System Inspired by Infectious Disease Ecology

Despite being man-made, cyber-power systems and the associated cyber-attacks evolve in tandem with human expertise, which matures over time. This dynamic mirrors the evolutionary processes observed in infectious disease ecology within microbial and human host systems. In the past, researchers have explored several bio-inspired, immunity-based approaches for cyber-attack defense, but these are not directly applicable to cyber-power systems. Although a few studies have applied bio-inspired methods in cyber-power systems, these were not focused on cybersecurity. Given the increasing integration of automation, communication, and computing technologies in power systems, it is essential to understand how bio-inspired, immunity-based approaches can be incorporated to build cyber-immunity in the cyber-power systems. In this work, we first explore the parallels between cyber-power systems and infectious disease ecology. Building on the analysis of similarities between different aspects of cyber-power security and infectious diseases, this work aims to develop a bio-inspired, resilient cyber defense system that can identify, protect, detect, respond to, and recover from complex, well-targeted cyber attacks in power systems. The proposed framework has two key features: 1) it draws upon principles and insights from both engineered and natural defense mechanisms used in combating infectious diseases, and 2) it evolves in response to cyber attacks by transmitting iterative feedback upon detecting specific attack stages. This iterative learning process enables the framework to defend against similar future attacks like the adaptive immune system of the human body. A realistic cyber-attack scenario has been developed using a real-time cyber-power substation automation testbed to validate the proposed bio-inspired framework. Our use case demonstrates that the framework can help defending against the initial steps of a cyber-attack and, through iterative learning, effectively counter all subsequent stages of similar attacks. The implementation of this bio-inspired framework will enable cyber-power systems to handle randomness, ambiguity, and rapidly evolving cyber threats while integrating digital substation automation technologies.