Intrusion Detection System using Cascade Forward Neural Network with Genetic Algorithm Based Feature Selection

Due to the rapid expansion and advancements of computer network, security has become a vital issue for modern computer network. The network intrusion detection systems play the vital role in protecting the computer networks. So, it has become a significant research issue. In spite of notable progress in intrusion detection system, there are still many opportunities to improve the existing systems in terms of false positives and classification accuracy. The objective of this research work is to reduce the false positive and increase the classification accuracy of intrusion detection system. In this paper, the Feed Forward Backpropgation Neural network and Cascade Forward Neural network are used to implement intrusion detection system. The NSL-KDD dataset is used to train and test these networks. The separate training and testing datasets are used to train and test the network. The Backpropgation algorithm based on Levenberg–Marquardt mechanism is used as training function. The networks were trained with 100 epochs. The results of Cascaded Forward neural network and Feed Forward Backpropgation Neural networks were evaluated in terms of classification accuracy, false positive rates, false negative and precision. Cascade Forward neural network exhibit the best results (Classification accuracy: 98.229737, False Positive rate: 0.0177, Precision rate: 0.9795) on cross validation. It exhibit the good results (Classification accuracy: 81.205642%, False Positive rate: 0.2931, Precision rate: 0.6978) on test dataset with 15 neurons in hidden layer. Feed Forward neural network exhibit 98.213860% Classification accuracy using cross validation and 76.530341% on test dataset with 25 neurons in hidden layer. Cascaded Forward Back propagation neural network model exhibits best results followed by Feed Forward neural network model.