Securing advanced metering infrastructure using intrusion detection system with data stream mining

Authors:
Mustafa Amir Faisal;Zeyar Aung;John R. Williams;Abel Sanchez
Affiliations:
Computing and Information Science Program, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates;Computing and Information Science Program, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates;Engineering Systems Division, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America;Engineering Systems Division, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
Venue:
PAISI'12 Proceedings of the 2012 Pacific Asia conference on Intelligence and Security Informatics
Year:
2012

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Abstract

Advanced metering infrastructure (AMI) is an imperative component of the smart grid, as it is responsible for collecting, measuring, analyzing energy usage data, and transmitting these data to the data concentrator and then to a central system in the utility side. Therefore, the security of AMI is one of the most demanding issues in the smart grid implementation. In this paper, we propose an intrusion detection system (IDS) architecture for AMI which will act as a complimentary with other security measures. This IDS architecture consists of three local IDSs placed in smart meters, data concentrators, and central system (AMI headend). For detecting anomaly, we use data stream mining approach on the public KDD CUP 1999 data set for analysis the requirement of the three components in AMI. From our result and analysis, it shows stream data mining technique shows promising potential for solving security issues in AMI.