Name
Abstract | ||
|---|---|---|
The increasing integration of Distributed Energy Resources (DER) networks in the smart grid highly affects its reliable and secure operation. DER communication protocols such as Distributed Network Protocol 3 (DNP3) lack basic security mechanisms such as encryption, authentication and authorization, making them vulnerable by design to confidentiality, integrity and availability attacks. This paper proposes a supervised machine learning-based anomaly detection model (ML-ADS) for detecting various stealthy cyber/physical attacks tailored for DER communication. The proposed system can identify anomalies at a fine granularity satisfying real-time latency requirements so that effective mitigations can be applied. To train the model, new DER DNP3-specific datasets were created and feature engineering was used to extract 92 DER physics and pattern-based traffic thresholds. For evaluation, the model was deployed and optimized into a physical low cost Edge Intelligent Device (EID) in a realistic hardware-in-the-loop (HIL) IEEE 39-bus smart grid DER environment. The proposed model achieved high detection accuracy (99.83%) with feasible latency (≈2 µs) for real-time deployment, with very low false-positive and false-negative rates of (0.008%) and (0.51%) respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CSR54599.2022.9850313 | 2022 IEEE International Conference on Cyber Security and Resilience (CSR) |
Keywords | DocType | ISBN |
CPS security,Machine Learning,IDS,Anomaly Detection,DER,DNP3,Smart Grid,Cybersecurity | Conference | 978-1-6654-9953-8 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moataz Abdelkhalek | 1 | 0 | 0.34 |
| Manimaran Govindarasu | 2 | 416 | 30.78 |