Biblio
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2021. Anomaly Detection Based on Edge Computing Framework for AMI. 2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT). :385—390.
Aiming at the cyber security problem of the advanced metering infrastructure(AMI), an anomaly detection method based on edge computing framework for the AMI is proposed. Due to the characteristics of the edge node of data concentrator, the data concentrator has the capability of computing a large amount of data. In this paper, distributing the intrusion detection model on the edge node data concentrator of the AMI instead of the metering center, meanwhile, two-way communication of distributed local model parameters replaces a large amount of data transmission. The proposed method avoids the risk of privacy leakage during the communication of data in AMI, and it greatly reduces communication delay and computational time. In this paper, KDDCUP99 datasets is used to verify the effectiveness of the method. The results show that compared with Deep Convolutional Neural Network (DCNN), the detection accuracy of the proposed method reach 99.05%, and false detection rate only gets 0.74%, and the results indicts the proposed method ensures a high detection performance with less communication rounds, it also reduces computational consumption.
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2021. Research on Node Anomaly Detection Method in Smart Grid by Beta Distribution Theory. 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). :755—758.
As the extensive use of the wireless sensor networks in Advanced Metering Infrastructure (AMI) of Smart Grid, the network security of AMI becomes more important. Thus, an optimization of trust management mechanism of Beta distribution theory is put forward in this article. First of all, a self-adaption method of trust features sampling is proposed, that adjusts acquisition frequency according to fluctuation of trust attribute collected, which makes the consumption of network resource minimum under the precondition of ensuring accuracy of trust value; Then, the collected trust attribute is judged based on the Mahalanobis distance; Finally, calculate the nodes’ trust value by the optimization of the Beta distribution theory. As the simulation shows, the trust management scheme proposed is suited to WSNs in AMI, and able to reflect the trust value of nodes in a variety of circumstances change better.