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.