A new Distance-based Feature Selection (DFS) approach in KDDCUP99 dataset for anomaly detection with Negative Selection Algorithm (NSA)

One of the most interesting and challenging task in intrusion detection system is feature selection.In this paper, a new distance-based feature selection approach is proposed in order to reduce the dimensions of KDDCUP99 dataset. One of the main problems of KDDCUP99 dataset is the curse dimensionality. So in order to solve this challengeable problem, feature selection algorithms are used widely. In this research, we use Euclidean distance to measure the distances between normal and abnormal data features. Our proposed feature selection algorithm, leads us to select 11features from 11 ones. To detect anomaly data, negative selection algorithm is used. The results shows better performance in anomaly detection compared with recent works

A new Distance-based Feature Selection (DFS) approach in KDDCUP99 dataset for anomaly detection with Negative Selection Algorithm (NSA) Keywords:

feature selection , dimensions , KDDCUP99 , anomaly detection , negative selection