Fusion of ANN and SVM classifiers for network attack detection

With the progressive increase of network application and electronic devices (computer, mobile phones, android, etc), attack and intrusion detection is becoming a very challenging task in cybercrime detection area. In this context, most of existing approaches of attack detection rely mainly on a finite set of attacks. However, these solutions are vulnerable, that is, they fail in detecting some attacks when sources of informations are ambiguous or imperfect. But, few approaches started investigating toward this direction. Following this trends, this paper investigates the role of machine learning approach (ANN, SVM)in detecting TCP connection traffic as normal or suspicious one. But, using ANN and SVM is an expensive technique individually. In this paper, combining two classifiers has been proposed, where artificial neural network (ANN) classifier and support vector machine (SVM) were employed. Additionally, our proposed solution allows to visualize obtained classification results. Accuracy of the proposed solution has been compared with other classifier results. Experiments have been conducted with different network connection selected from NSL-KDD DARPA dataset. Empirical results show that combining ANN and SVM techniques for attack detection is a promising direction.