Intrusion Detection Method Based On Computational Intelligence Independent Network

In recent years, with the rapid development of network technology, network plays an enormous role in our lives, however, network risk and network attacks can be seen everywhere. With the development of networks, attacks have also become increasingly complex and diversified. Traditional network security technology, such as firewall, can not ensure the safety of confidential information in networks. How to protect information in networks becomes the focus of the research. As a pro-active and effective method, Intrusion Detection Technology is being more and more emphasized.Intrusion Detection System (IDS) has been developing rapidly, a lot of tremendous contributions have been made to the safety of the network. However, most of current IDS are rule-based detection, they can detect known intrusion accurately, but it is difficult to detect new type of abnormalities. Therefore, the research of developing a new Intrusion Detection System, which can detect new type of intrusions, is of great significance.On the basis of above background, we have studied and analyzed the intrusion detection technology in recent years, and focused on data-mining and information spreading technology. Finally, a new method of intrusion detection based on Computational Intelligence was presented in this paper. It is proved effective on specific environment. Main content of this paper are:1. Get the network service sequence from a pure network environment, and classify network service sequence to extract the normal characteristics of the frequent episode rules. In the promiscuous environment, extract frequent episode. Find out the abnormal behaviors by using a sliding window approach on the sequence.2. Identify the abnormal behavior and calculate the abnormal score by using swarm intelligence methods. Extract the signature of the abnormal sequence and release to other nodes in the local networks. Artificial Immune technology is used for managing the existed signature in system.At last, simulation experiment is carried out on the data sets of KDDCUP99. Experiment result shows about 60% of the new attacks in dataset can be recognized.