Anomaly detection for remote maintenance control system of natural gas pipeline based on nearest neighbor clustering
Kangsheng Li, Quan Yuan, Bowen Jiang, Yulong Xian, Xin D. Gao · 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022) · 2022
Accurate anomaly detection of remote maintenance control system of natural gas pipeline is of great significance in ensuring the safe and stable operation of gas pipeline network. A supervised anomaly detection method based on k-nearest neighbors searching and clustering is proposed. Firstly, the labels of neighbor samples are used to determine whether the sample is noise or belongs to an anomaly cluster, and the iterative search is conducted in the neighbor samples until no more abnormal samples belonging to this cluster are found. Then, the noise samples are filtered and the numbers of new abnormal samples to be generated in each cluster are calculated. Finally, SMOTE is used to generate the artificial samples in each cluster to balance the data. The experiment results on public datasets and the remote maintenance control system monitoring dataset show that the proposed method outperforms the compared methods in terms of F-measure and G-mean.