EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection
A Niranjan, K M Akshobhya, P. Deepa Shenoy, K R Venugopal · Advances in Science Technology and Engineering Systems Journal · 2019
Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data.Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection.The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency.This work is an extension of our previous work ERCRTV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification using Voting.In the current work, we propose SDMR a simple Feature Selection Technique to select significant features from the data set.Furthermore, to reduce the dimensionality, we use PCA in the pre-processing stage.The EKMC (Ensemble of kNN using MetaCost) with ten-fold cross validation is then applied on the pre-processed data.The performance of EKMC is evaluated on UNSW_NB15 and NSL KDD data sets.The results of EKMC indicate better detection rate and prediction accuracy with a lesser error rate than other existing methods.