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Model-based intrustion detection for the smart grid (MINDS)

Current concerns for the cyber security of the smart grid require the development of novel attack detection tools. Intrusion detection systems have proven to be a critical component of traditional IT architectures, however, current techniques do not adequately meet the stringent requirements of the electric grid. This research introduces a model-based intrusion detection system specifically targeting the operations of smart grid environments, especially substation automation systems. This approach focuses on the IEC 61850 protocol and leverages the deterministic data flows to accurately identify communication patterns. These data flows are then represented with a Petri-net model which is used to identify malicious spacial and temporal anomalies. Finally, this research proposes system level analysis of detected substation attacks to help identify potential coordinated attacks.