원문정보
초록
영어
Many factors could influence the clustering performance of K-means algorithm, selection of initial cluster centers was an important one, traditional method had a certain degree of randomness in dealing with this problem, for this purpose, information entropy was introduced into the process of cluster centers selection, and a fusion algorithm combining with information entropy and K-means algorithm was proposed, in which, information entropy value was used to measure the similarity degree among records, the least similar record would be regarded as a cluster center. In addition, a network intrusion detection model was built, it could make cluster centers change dynamically along with the network changes, and the model could real-time update the cluster centers according to actual needs. Experiment results show that the improved algorithm proposed is better than the traditional K-means algorithm in detection ratio and false alarm ratio, and the network intrusion detection model is proved to be feasible.
목차
1. Introduction
2. Fusion Algorithm Combing with Information Entropy and K-means
2.1. K-means Algorithm
2.2. Information Entropy
2.3. IE-K-means Algorithm based on Information Entropy
2.4. Network Intrusion Detection Algorithm Based on IE-K-means
3. Network Intrusion Detection Model based on IE-K-means Algorithm
3.1. Data Standardization Processer
3.2. IE-K-means Clustering Tool
3.3. Anomaly Detection System
3.4. Updater of Cluster Centers
4. Simulation Experiment and Analysis
5. Conclusions
Acknowledgements
References