Mitigating False Negative intruder decisions in WSN-based Smart Grid monitoring

Monitoring the Smart Grid (SG) is highly desired for critical applications such as power quality assessment and transformer monitoring. Due to their low-cost, flexibility and efficiency as well as their widely usage in several critical infrastructure monitoring applications, Wireless Sensor Networks (WSNs) are estimated to be extensively used in SG applications. WSNs-based SG networks are vulnerable to different types of attacks and intruders. In order to operate networks in secured environments, in this paper we analyze our Clustered Hierarchal Hybrid-Intrusion Detection System (CHH-IDS) that is responsible for various attacks injected by known and unknown intruders. As False Positives (FPs) and False Negatives (FNs) are the key performance parameters in IDS, we investigate mitigation of FNs through a two-tier intrusion detection approach, which deals with anomaly and signature detection in parallel. In the presence of such a hybrid mode, utilization proportion between the anomaly detection and signature detection models affect the FN performance. In these two subsystems, Random Forest method is used for signature detection over known attacks and E-DBSCAN (Enhanced Density-Based Spatial Clustering of Applications with Noise) method is used for anomaly detection over unknown attacks. Through simulations that run on real datasets, we validate that the higher the weight of anomaly detection subsystem (i.e. the lower the weight of the signature detection subsystem), the lower the FN rates experienced by the entire H-IDS system. More specifically, we show that FN rates can be significantly reduced by 20.4% when the weight on anomaly detection subsystem is increased from 60% to 70% while the accuracy is expected to be improved through signature detection subsystem by using the Random Forest which has higher detection rate than the E-DBSCAN method.