Multidimensional Clustering Based Anomaly Detection Research

Network anomaly detection which is a very important issue in network management has been extensively studied in recent years.Although people in the field made a number of advanced works,the accuracy of automatic classification of network traffic to detect and identify abnormal network traffic is still a very challenging problem.It presents a multidimensional clustering based anomaly detection method,by two stages to achieve anomaly detection.The first phase,through multidimensional clustering algorithms,network traffic is automatically mined into different multidimensional clusters.The second phase calculates the degree of multidimensional clusters to achieve anomaly detection.By this method,the abnormal network traffic is automatically classified into different meaningful clusters,and then these clusters can be used to find network anomalies.Finally,this algorithm was validated through experiments,the results show that the method can effectively identify abnormal network traffic.