Efficient Anomaly Detection Algorithm for Operational Data Based on Fuzzy Cognitive Map

In order to improve the accuracy and efficiency of anomaly detection in operational data, an efficient anomaly detection algorithm for operational data based on fuzzy cognitive maps is proposed. Clean, denoise, and standardize raw operational data; Extracting concept nodes based on fuzzy cognitive maps; Constructing a fuzzy cognitive map model based on relational mapping to obtain the probability distribution results of operational data; Design an efficient anomaly detection algorithm for operational data through big data information fusion and association rule mining methods. The test results show that this method can improve the sensitivity and detection rate of abnormal data detection, reduce false detection rate, KL divergence, and average detection delay time. The accuracy and efficiency of abnormal detection in operational data are high.