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Machine Learning Based Physical-Layer Intrusion Detection and Location for the Smart Grid | IEEE Conference Publication | IEEE Xplore

Machine Learning Based Physical-Layer Intrusion Detection and Location for the Smart Grid


Abstract:

Security and privacy of smart grid communication data is crucial given the nature of the continuous bidirectional information exchange between the consumer and the utilit...Show More

Abstract:

Security and privacy of smart grid communication data is crucial given the nature of the continuous bidirectional information exchange between the consumer and the utilities. Data security has conventionally been ensured using cryptographic techniques implemented at the upper layers of the network stack. However, it has been shown that security can be further enhanced using physical layer (PHY) methods. To aid and/or complement such PHY and upper layer techniques, in this paper, we propose a PHY design that can detect and locate not only an active intruder but also a passive eavesdropper in the network. Our method can either be used as a stand-alone solution or together with existing techniques to achieve improved smart grid data security. Our machine learning based solution intelligently and automatically detects and locates a possible intruder in the network by reusing power line transmission modems installed in the grid for communication purposes. Simulation results show that our cost-efficient design provides near ideal intruder detection rates and also estimates its location with a high degree of accuracy.
Date of Conference: 21-23 October 2019
Date Added to IEEE Xplore: 25 November 2019
ISBN Information:
Conference Location: Beijing, China

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