Multi-Source Data Fusion for Cyberattack Detection in Power Systems

• URL: http://arxiv.org/abs/2101.06897v1
• Date: Mon, 18 Jan 2021 06:34:45 GMT
• Title: Multi-Source Data Fusion for Cyberattack Detection in Power Systems
• Authors: Abhijeet Sahu and Zeyu Mao and Patrick Wlazlo and Hao Huang and Katherine Davis and Ana Goulart and Saman Zonouz
• Abstract summary: We show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle attacks.
• Score: 1.8914160585516038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features.

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