Intelligent analysis of digital evidences in large-scale logs in power systems attributed to the attacks

Smart grid improves and revolutionizes the way how energy is generated, distributed and consumed. Despite utilization of such technologies for better life of end-users and communities, there might be outlier events happening that will introduce disturbance to the smart grids. To mitigate impact from such events in power grid, particularly in Wide Area Monitoring Protection and Control (WAMPAC) has been introduced for mitigation and prevention of large disruption and extreme events. Large network of interconnected devices is being monitored through WAMPAC sub-system to avoid major events with negative impact through analysis of system-wide contextual information. The assessment of the state is being made based on the data from Phasor Measurement Unit (PMUs) collected and processed in the Phasor Data Concentrator (PDC). There is an enormous amount of Machine-to-Machine (M2M) communication that the system has to analyze. However, blackout prediction and mitigation is done using measurements data and does not necessarily focus on more high level adversarial events. This paper proposes an ongoing research into timely detection of adversarial attack on the power grid. During the experimental phase, authentication attack scenario was successfully executed on power substation setup. Further, framework for intelligent identification of digital evidences related to attack was suggested unveiling possibility for crime investigations preparedness.