Abstract
Integrating information and communication technologies into the power generation, transmission and distribution system provides a new concept called Smart Grid (SG). The wide variety of devices connected to the SG communication infrastructure generates heterogeneous data with different Quality of Service (QoS) requirements and communication technologies. An intrusion Detection System (IDS) is a surveillance system monitoring the traffic flow over the network, seeking any abnormal behaviour to detect possible intrusions or attacks against the SG system. Distributed fashion of power and data in SG leads to an increase in the complexity of analysing the QoS and user requirements. Thus, we require a Big Data-aware distributed IDS dealing with the malicious behaviour of the network. Motivated by this, we design a distributed IDS dealing with anomaly big data and impose the proper defence algorithm to alert the SG. This paper proposes a new smart meter (SM) architecture, including a distributed IDS model (SM-IDS). Secondly, we implement SM-IDS using supervised ML algorithms. Finally, a distributed IDS model is introduced using federated learning. Numerical results approve that Neighbourhood Area Network IDS (NAN-IDS) can help decrease smart meters' energy and resource consumption. Thus, SM-IDS achieves an accuracy of 84.31% with a detection rate of 74.69%. Also, NAN-IDS provides an accuracy of 87.40% and a detection rate of 86.73%.