Intrusion Detection using Deep Belief Network

Abstract

This paper proposes an intrusion detection technique based on DBN (Deep Belief Network) to classify four intrusion classes and one normal class using KDD-99 dataset. The proposed technique is based on two phases: in first phase it removes the class imbalance problem and in the next, it applies DBN followed by FFNN (Feed-Forward Neural Network) to build a prediction model. The obtained results are compared with those given in [9]. The prediction accuracy of our model shows promising results on both intrusion and normal pattern