Hierarchical Takagi-Sugeno Models for Online Security Evaluation Systems

摘要：

Risk assessment is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process cannot be expressed precisely. This paper propose a light weight risk assessment system based on an Hierarchical Takagi-Sugeno model designed using evolutionary algorithms. Performance comparison is done with neuro-fuzzy and genetic programming methods. Empirical results indicate that the techniques are robust and suitable for developing light weight risk assessment models, which could be integrated with intrusion detection and prevention systems.

作者: Ajith Abraham Crina Grosan Hongbo Liu Yuehui Chen

作者单位: Machine Intelligence Research Labs,MIR Labs Machine Intelligence Research Labs,MIR Labs;Babes-Bolyai University,Cluj-Napoca,Romania Machine Intelligence Research Labs,MIR Labs;School of Computer Science,Dalian Maritime University,11 Machine Intelligence Research Labs,MIR Labs;School of Information Science and Engineering,Jinan Univ

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 687-692

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）