A Mathematical Model for Anomaly Detection in Uncertain Multiple Data Streams
With the rapid development of Internet technology,the application of data streams is becoming more and more popular,and the demand of communication platforms for anomaly detection of multiple data streams is also growing.In order to solve the problems of low accuracy and difficult feature extraction of current anomaly detection al-gorithms,this paper proposes a grid based multi data stream anomaly detection algorithm.The algorithm first extracts the features of uncertain multi data streams,and by analyzing the distribution status of the data streams,abnormal data is extracted;Then,a grid based method is used to partition multiple data streams,and abnormal data is extracted by calculating the grid anomaly factor,achieving the effect of anomaly detection;Finally,for abnormal data,correlation a-nalysis of variable factors is conducted to reduce false positives and improve the accuracy of anomaly detection.The experimental results show that the algorithm proposed in this paper improves the accuracy of anomaly detection by a-bout 4%,reduces the rate of missed detection by at least 3%,and reduces the false detection rate by more than 8%.It effectively improves the accuracy of anomaly detection and reduces the negative impact of abnormal data flow on work and life.
Multiple data streamsAbnormal detectionMathematical modelAbnormal factor
NETL
NSTL
万方数据
