Wk-fnn design for detection of anomalies in the computer network traffic

Abstract

Anomaly-based intrusion detection systems identify abnormal computer network traffic based on deviations from the derived statistical model that describes the normal network behavior. The basic problem with anomaly detection is deciding what is considered normal. Supervised machine learning can be viewed as binary classification, since models are trained and tested on a data set containing a binary label to detect anomalies. Weighted k-Nearest Neighbor and Feedforward Neural Network are high-precision classifiers for decision-making. However, their decisions sometimes differ. In this paper, we present a WK-FNN hybrid model for the detection of the opposite decisions. It is shown that results can be improved with the xor bitwise operation. The sum of the binary ?ones? is used to decide whether additional alerts are activated or not.