A genetic clustering method for intrusion detection |
| |
Authors: | Yongguo Liu [Author Vitae] Kefei Chen [Author Vitae] [Author Vitae] Wei Zhang [Author Vitae] |
| |
Affiliation: | a Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China b Department of Computer Science and Engineering, Chongqing University, Chongqing 400044, China c Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing 400067, China |
| |
Abstract: | Traditional intrusion detection methods lack extensibility in face of changing network configurations as well as adaptability in face of unknown attack types. Meanwhile, current machine-learning algorithms need labeled data for training first, so they are computational expensive and sometimes misled by artificial data. In this paper, a new detection algorithm, the Intrusion Detection Based on Genetic Clustering (IDBGC) algorithm, is proposed. It can automatically establish clusters and detect intruders by labeling normal and abnormal groups. Computer simulations show that this algorithm is effective for intrusion detection. |
| |
Keywords: | Intrusion detection Clustering Genetic algorithms Simulated annealing |
本文献已被 ScienceDirect 等数据库收录! |
|