  Intrusion detection is one of the important mechanisms that provide computer
networks security. Due to an increase in attacks and growing dependence upon
other fields such as medicine, commerce, and engineering, offering services
over a network and maintaining network security have become a significant
issue. The purpose of Intrusion Detection Systems (IDS) is to develop models
which are able to distinguish regular communications from abnormal ones, and
take the necessary actions. Among different methods in this field, Artificial
Neural Networks (ANNs) have been widely used. However, ANN-based IDS
encountered two main problems: low detection precision and weak detection
stability. To overcome these problems, this paper proposes a new approach based
on Deep Neural Network ans Support vector machine classifier, which inspired by
"divide and conquer" philosophy. The proposed model predicts the attacks with
better accuracy for intrusion detection rather similar methods. For our
empirical study, we were taking advantage of the KDD99 dataset. Our
experimental results suggest that the new approach enhance to 95.4 percent
classification accuracy.
