Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid: A feasibility study

sponsible for collecting, measuring, and analyzing energy usage data, as well as transmitting these information from a smart meter to a data concentrator and then to a headend system in the utility side, the security of AMI is of great concern in the deployment of Smart Grid (SG). In this paper, we analyze the possibility of using data stream mining for enhancing the security of AMI through an Intrusion Detection System (IDS), which is a second line of defense after the primary security methods of encryption, authentication, authorization, etc. We propose a realistic and reliable IDS architecture for the whole AMI system which consists of individual IDSs for three different levels of AMI’s components: smart meter, data concentrator, and AMI headend. We also explore the performances of various existing state-of-the-art data stream mining algorithms on a publicly available IDS dataset, namely, the KDD Cup 1999 dataset. Then, we conduct a feasibility analysis of using these existing data stream mining algorithms, which exhibits varying levels of accuracies, memory requirements, and running times, for the distinct IDSs at AMI’s three different components. Our analysis identifies different candidate algorithms for different AMI components ’ IDSs respectively. Index Terms—Smart grid, advanced metering infrastructure