Network Intrusion Detection Model Based on Convolutional Neural Network

Network intrusion detection is an important research direction of network security. The diversification of network intrusion mode and the increasing amount of network data make the traditional detection methods can not meet the requirements of the current network environment. The development of deep learning technology and its successful application in the field of artificial intelligence provide a new solution for network intrusion detection. In this paper, the convolutional neural network in deep learning is applied to network intrusion detection, and an intelligent detection model which can actively learn is established. The experiment on KDD99 data set shows that it can effectively improve the accuracy and adaptive ability of intrusion detection, and has certain effectiveness and advancement.

I. Introduction

Nowadays, one of the biggest threats to network security is network intrusion. Network intrusion refers to any behavior that threatens or endangers the availability, confidentiality and integrity of the host or network. It is the foundation of all network destruction. Network intrusion detection as an important means of network intrusion prevention, it can judge the intrusion behavior according to the abnormal data of network or host, and achieve the purpose of identifying intrusion behavior. However, the traditional network intrusion detection methods can only detect the new network intrusion passively afterwards, which can not solve the problems of various network intrusion modes and unknown intrusion detection. Research on more accurate and efficient network intrusion detection method has become an urgent task to solve the problem of network security [1].