Feature Selection in large multi-dimensional data sets is becoming increasingly important for several real world applications. One such application, used by network administrators, is Network Intrusion Detection. The major problem with anomaly based intrusion detection systems is high number of false positives. Motivated by such a requirement, we propose sv(M)kmeans: a two step hybrid feature selection technique. The proposed technique applies classification on false-positives and true positives; and on false-positives and true-negatives after an initial round of clustering. Specifically, SVM-RFE is applied on the results obtains from MK-Means. sv(M)kmeans is evaluated for its real world applicability using the benchmark NSL-KDD data set. We show the feature subset to significantly reduce the false positive rate and increase the accuracy in network anomaly detection.
['Shubham Saini', 'Shraey Bhatia', 'I. Sumaiya Thaseen']
sv(M)kmeans: a hybrid feature selection technique for reducing false positives in network anomaly detection