| Title | Realistic measurement protection schemes against false data injection attacks on state estimators |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Wang, J., Shi, D., Li, Y., Chen, J., Duan, X. |
| Conference Name | 2017 IEEE Power Energy Society General Meeting |
| Keywords | alternative measurement protection schemes, Computing Theory, cost-benefit analysis, Cyber-physical security, false data injection attack, false data injection attacks, FDIA, graph theory, ignored cost-benefit issue, imminent cyber-physical security issue, Indexes, maximum total ROI, metric return, Metrics, optimisation, Optimization, performance evaluation, power engineering computing, power system security, power system state estimation, pubcrawl, realistic measurement protection schemes, realistic MPS, return on investment, risk management, security metrics, security of data, Smart grids, state estimation, state estimators, Steiner tree problem, Steiner trees, trees (mathematics) |
| Abstract | False data injection attacks (FDIA) on state estimators are a kind of imminent cyber-physical security issue. Fortunately, it has been proved that if a set of measurements is strategically selected and protected, no FDIA will remain undetectable. In this paper, the metric Return on Investment (ROI) is introduced to evaluate the overall returns of the alternative measurement protection schemes (MPS). By setting maximum total ROI as the optimization objective, the previously ignored cost-benefit issue is taken into account to derive a realistic MPS for power utilities. The optimization problem is transformed into the Steiner tree problem in graph theory, where a tree pruning based algorithm is used to reduce the computational complexity and find a quasi-optimal solution with acceptable approximations. The correctness and efficiency of the algorithm are verified by case studies. |
| DOI | 10.1109/PESGM.2017.8274291 |
| Citation Key | wang_realistic_2017 |
Realistic measurement protection schemes against false data injection attacks on state estimators