Comparison deep learning method to traditional methods using for network intrusion detection

Recently, deep learning has gained prominence due to the potential it portends for machine learning. For this reason, deep learning techniques have been applied in many fields, such as recognizing some kinds of patterns or classification. Intrusion detection analyses got data from monitoring security events to get situation assessment of network. Lots of traditional machine learning method has been put forward to intrusion detection, but it is necessary to improvement the detection performance and accuracy. This paper discusses different methods which were used to classify network traffic. We decided to use different methods on open data set and did experiment with these methods to find out a best way to intrusion detection.

I. Introduction

Deep learning has in recent times gained prominence due to the potential it portends for machine learning. Of importance, the science has become an integral part of network security since it ensures exhaustive and conclusive evaluation of the network security system. To start with, deep learning can be defined as the use of deep networks that are linked to calculate algorithms that in turn use several layers to produce an output. In essence, the layers cascade with the next tier using the results from the previous phase as input in order for it to produce an output.