Research on performance optimizations for TCM-KNN network anomaly detection algorithm - LI Yang1; GUO Li1; LU Tian-bo3; TIAN Zhi-hong1 (Tongxin xuebao)
Based on TCM-KNN（transductive confidence machine for K-nearest neighbors） algorithm,the filter-based feature selection and cluster-based instance selection methods were used towards optimizing it as a lightweight network anomaly detection scheme,which not only reduced its complex feature space,but also acquired high quality instances for training.A series of experimental results demonstrate the two methods for optimizations are actually effective in greatly reducing the computational costs while ensuring high detection performances for TCM-KNN algorithm.Therefore,the two methods make TCM-KNN be a good scheme for a lightweight network anomaly detection in practice.