  Application of deep learning to enhance the accuracy of intrusion detection
in modern computer networks were studied in this paper. The identification of
attacks in computer networks is divided in to two categories of intrusion
detection and anomaly detection in terms of the information used in the
learning phase. Intrusion detection uses both routine traffic and attack
traffic. Abnormal detection methods attempt to model the normal behavior of the
system, and any incident that violates this model is considered to be a
suspicious behavior. For example, if the web server, which is usually passive,
tries to There are many addresses that are likely to be infected with the worm.
The abnormal diagnostic methods are Statistical models, Secure system approach,
Review protocol, Check files, Create White list, Neural Networks, Genetic
Algorithm, Vector Machines, decision tree. Our results have demonstrated that
our approach offers high levels of accuracy, precision and recall together with
reduced training time. In our future work, the first avenue of exploration for
improvement will be to assess and extend the capability of our model to handle
zero-day attacks.
