Anomaly Intrusion Detection Approach Using Hybrid MLP/CNN Neural Network

Abstract

An anomaly intrusion detection approach based on hybrid MLP/CNN (multi-layer perceptron/chaotic neural network) neural network is proposed in this paper. Most anomaly detection approaches using MLP can detect novel real-time attacks, but still has high false alarm rates. Most attacks are composed of a series of anomaly events. These attacks are called time-delayed attacks, which current neural network IDSs (intrusion detection system) cannot identify efficiently. A hybrid MLP/CNN neural network is constructed in order to improve the detection rate of time-delayed attacks. While obtaining a similarly detection rate of real-time attacks as the MLP does, the proposed approach can detect time-delayed attacks efficiently with chaotic neuron. This approach also exhibits a lower false alarm rate when detects novel attacks. The simulation tests are conducted using DARPA 1998 dataset. The experimental results are presented and compared in ROC curves, which can demonstrate that the proposed approach performs exceptionally in terms of both detection rate and false alarm rate