| Title | Anomaly Detection Based on Edge Computing Framework for AMI |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liang, Haolan, Ye, Chunxiao, Zhou, Yuangao, Yang, Hongzhao |
| Conference Name | 2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT) |
| Date Published | jul |
| Keywords | Advanced Metering Infrastructure(AMI), anomaly detetion method, composability, Computational modeling, data centers, Data models, data privacy, Deep Convolutional Neural Network (DCNN), Distributed databases, edge computing framework, edge detection, Image edge detection, Intrusion detection, KDDCUP99 datasets, Metrics, pubcrawl, resilience, Resiliency, Scalability, security |
| Abstract | 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. |
| DOI | 10.1109/ICEEMT52412.2021.9601888 |
| Citation Key | liang_anomaly_2021 |
Anomaly Detection Based on Edge Computing Framework for AMI