Detecting Anomalies using Overlapping Electrical Measurements in Smart Power Grids

As cyber-attacks against critical infrastructure be-come more frequent, it is increasingly important to be able to rapidly identify and respond to these threats. This work investigates two independent system with overlapping electrical measurements with the goal to more rapidly identify anomalies. The independent systems include HIST, a SCADA historian, and ION, an automatic meter reading system (AMR). While prior research has explored the benefits of fusing measurements, the possibility of overlapping measurements from an existing elec-trical system has not been investigated. To that end, we explore the potential benefits of combining overlapping measurements both to improve the speed/accuracy of anomaly detection and to provide additional validation of the collected measurements. In this paper, we show that merging overlapping measurements provide a more holistic picture of the observed systems. By applying Dynamic Time Warping more anomalies were found – specifically, an average of 349 times more anomalies, when considering anomalies from both overlapping measurements. When merging the overlapping measurements, a percent change of anomalies of up to 785% can be achieved compared to a non-merge of the data as reflected by experimental results.