Hybrid System of Learning Vector Quantization and Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Classification

Network-based computer systems play increasingly vital roles in modern society; they have become the target of intrusions by our enemies and criminals. Intrusion detection system attempts to detect computer attacks by examining various data records observed in processes on the network. This paper presents a hybrid intrusion detection system models, using Learning Vector Quantization and an enhanced resilient backpropagation artificial neural network. The proposed system is divided into five phases: environment phase, dataset features and preprocessing phase, Learning Vector Quantization phase, enhanced resilient backpropagation neural network phase and testing the hybrid system phase. A Supervised Learning Vector Quantization (LVQ) as the first stage of classification was trained to detect intrusions; it consists of two layers with two different transfer functions, competitive and linear. A multilayer perceptron as the second stage of classification was trained using an enhanced resilient backpropagation training algorithm. Best number of hidden layers and hidden neurons were calculated to train the enhanced resilient backpropagation neural network. One hidden layer with 32 hidden neurons was used in resilient backpropagation artificial neural network training process. An optimal learning factor was derived to speed up the convergence of the resilient backpropagation neural network performance. The evaluations were performed using the NSL-KDD99 network anomaly intrusion detection dataset. The experiments results demonstrate that the proposed system (LVQ_ERBP) has a detection rate about 97.06% with a false negative rate of 2%.