Research On Intrusion Detection Based On Feature Selection And Clustering

In the modern society, with the development of computer and communication technology, computers are widely used, but network security issues are also increasing prominently. Traditional security measures such as firewalls, data encryption can not fully meet the needs of network security. Intrusion detection is a new kind of security technologies, as opposed to traditional security measures, it is a technology based on active defense, it can detect intrusion and exceptions before the network system suffers the hazards, and make appropriate response. The key of intrusion detection is to effectively collecting data and analyzing a variety of behaviors. However, as well as the growing of all kinds of attacks and destructions, the massing of network data brings great difficulties to intrusion detection. The introduction of data mining provides a good means for intrusion detection. The past intrusion detection based on data mining method requires the training set data and the data sample which has been labeled. Clustering is an unsupervised learning method; you can establish the detection model or discover abnormal data on unlabeled dataset, so it can overcome the shortcomings of traditional data mining methods.Based on the above study background, this paper carried out research on intrusion detection based on clustering technology. First introduced intrusion detection technology and clustering and analyzed the clustering algorithm in intrusion detection. In view of the problems that exit in the traditional fuzzy C-means clustering during the application of intrusion detection, such as sensitive to initial value, easy to fall into local optimum, we introduce particle swarm optimization algorithm with cross-operation to combine with it, forming a modified fuzzy C-Means algorithm. Using KDD CUP 1999 data set to test the improved algorithm, the experimental results show that the algorithm has better intrusion detection.Feature selection is widely used in dimension reduction and removal of irrelevant features, it is generally used as a classification preprocessing step, by eliminating irrelevant and redundant features, it can avoid dimension disaster, and improve processing speed and reduce the computational cost. Feature selection in intrusion detection is necessary for the high dimensional and complex features of intrusion detection data. This paper presents a feature selection method based on particle swarm and clustering. The results of experiment using KDD CUP 1999 show that the algorithm can speed up the rate of feature selection and the selected feature subset has better classification results.