| As a kind of active defense technology, intrusion detection technology detects sorts of malicious attacks in time and responds when the net system is endangered. It is a reasonable supplement to traditional security technology such as firewall. As a new network security technology, intrusion detection technology has become the major concern of network security researching field nowadays.But there are some problems in current intrusion detection technique. Armed at these issues, this article proposes a general intrusion detection system based on machine learning and gives the system model architecture diagram, the main flow steps of the system model, then design and test the machine learning module, this module mainly applies the neural network that belongs to machine learning, BP algorithm that applying in the intrusion detection system model.This article also researches on the intrusion detection system-Snort, which is based on the network and misuse, and gives emphasis on the analysis of the whole system source codes. Then a plan that adding the machine learning to the Snort is put forward, so the machine learning-based Snort system can not only detect the known attacks by pattern matching, but also detect the unknown attacks by self learning. |
