Intrusion detection system based on Multi-Layer Perceptron Neural Networks and Decision Tree

The growth of internet attacks is a major problem for today's computer networks. Hence, implementing security methods to prevent such attacks is crucial for any computer network. With the help of Machine Learning and Data Mining techniques, Intrusion Detection Systems (IDS) are able to diagnose attacks and system anomalies more effectively. Though, most of the studied methods in this field, including Rule-based expert systems, are not able to successfully identify the attacks which have different patterns from expected ones. By using Artificial Neural Networks (ANNs), it is possible to identify the attacks and classify the data, even when the dataset is nonlinear, limited, or incomplete. In this paper, a method based on the combination of Decision Tree (DT) algorithm and Multi-Layer Perceptron (MLP) ANN is proposed which is able to identify attacks with high accuracy and reliability.