  With the progressive increase of network application and electronic devices
(computers, mobile phones, android, etc.) attack and intrusion, detection has
become a very challenging task in cybercrime detection area. in this context,
most of the existing approaches of attack detection rely mainly on a finite set
of attacks. These solutions are vulnerable, that is, they fail in detecting
some attacks when sources of informations are ambiguous or imperfect. However,
few approaches started investigating in this direction. This paper investigates
the role of machine learning approach (ANN, SVM) in detecting a TCP connection
traffic as a normal or a suspicious one. But, using ANN and SVM is an expensive
technique individually. In this paper, combining two classifiers are proposed,
where artificial neural network (ANN) classifier and support vector machine
(SVM) are both 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 connections selected from NSL-KDD DARPA
dataset. Empirical results show that combining ANN and SVM techniques for
attack detection is a promising direction.
