Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This paper presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaningbased methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. We have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

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Development of a smarter electric grid necessitates addressing the associated cyber security challenges. Since the interdependence between the legacy grid infrastructure and advanced information technology is growing rapidly, there are numerous ways advanced, motivated, and persistent attackers can affect the SCADA based critical infrastructure. Hence, developing a security information and event management (SIEM) is crucial for securing the SCADA power system. This paper presents the application of Security Onion (SecOn) to develop the network security monitoring (NSM) and intrusion detection system (IDS) in the context of SCADA cyber physical security. Initially, we have applied a cyber kill-chain model to demonstrate the different stages of attacks and associated mechanisms. Later, the rulebased IDS (RIDS) is developed using Snort IDS, and tested in the cyber-physical SCADA environment. Furthermore, we have evaluated its performance in terms of accuracy and detection latency. Our experimental results reveal that the SecOn tool is efficient in monitoring and detecting attacks within an acceptable time frame with a high accuracy rate. Disciplines Disciplines Electrical and Computer Engineering Comments Comments This proceeding is published as Singh, Vivek Kumar, Steven Perez Callupe, and Manimaran Govindarasu. "Testbed-based Evaluation of SIEM Tool for Cyber Kill Chain Model in Power Grid SCADA System." (2019). Posted with permission.

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