Outlier Identification using Symmetric Neighborhoods

In recent research, outlier mining has been widely used in areas such as telecommunication, health care, finance and network intrusion detection. By applying outlier mining in network anomaly detection, rarely occurring attacks can be identified. In this paper we propose an outlier mining method based on symmetric neighborhood relationships. We evaluate the method with UCI ML Repository datasets, benchmark dataset KDD Cup 1999 and real time intrusion datasets. The experimental results are compared with existing approaches and performance is excellent.