Cyber-physical System Modelling for Cyber-security Analysis in Electricity Systems

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This PhD project aims to develop a holistic cyber-physical modelling framework for electricity system in order to analyse the impact of cyber-security on the reliability and resilience of future low carbon systems. Specifically, the project aims to enhance cyber-physical security in power systems through the development of novel forms of cyber-attack and proprietary forms of defence. This project aims to have direct applications for the security of power systems. In a sense the work is multi-disciplinary interacting with the ESPRC strategic themes of Energy, Engineering and ICT with research area applications across control engineering, ICT, digital signal processing and energy networks. Specifically, the studentship will focus on the problem of False Data Injection attacks and how they can be circumvented using the physical system assets themselves via novel application of Moving Target Defences (MTDs). Areas of focus will include target selection, attack detection via probabilistic and analytics methods, developing fall-back states in case of attack and the application of data analytics with respect to cyber-attacks. To implement the research the student will first engage in literature review of the state-of-the-art journal and conference papers in the field of cyber-security for power systems. They will then devise a mathematical formulation for the problem they wish to solve and use power system simulation tools (such as MATPOWER and Simulink) to create models of systems under attack and evaluate appropriate defence strategies. In the first year of the PhD, a journal paper has been submitted for review to IEEE transactions on making use of dimensionality reduction and DBSCAN clustering to enhance FDI attacks. The paper (currently subject to 2nd round of corrections) made contributions in both the attack and defence of power systems and focused on enhancing cyber-attacks by countering conventional MTD and employing new ways of enhancing MTD to be resilient. The paper focused on using system assets already in place to drive attack detection of sophisticated cyber-attacks and reduce the overall cost to the system from the application of defence.Going forward, collaboration with other institutions will be explored with the development of a specialised topology learning cyber-attack in combination with Tsinghua University. This work will build on a number of exciting areas and may culminate with a foreign student exchange toward the end of the 2nd year. Also, considerations for potential commercialisation of the research will also be made. Cyber-Security as a field has received much focus recently with accelerators such as CyberASAP offering venues for academics to bring research to market. The prospect of developing a low cost Multos based analytic tool for distributed state estimation and attack detection will be explored and a conference paper published in this area.