摘要
针对当前网络安全管理的复杂性和态势感知过程缺乏自适应性等问题,提出一个基于自律计算的网络安全态势感知模型。利用自律反馈机制对态势提取进行实时分析;根据提取的态势信息,从攻击和防御两个角度出发,采用层次分析法建立多层次多角度的网络安全态势评估模型;依据过去和当前网络安全态势,采用改进的遗传神经网络方法建立网络安全态势预测模型。仿真实验结果表明,具有自律反馈机制的态势感知模型可以有效增强系统的自适应能力。
Concerning the complexity of network security management and the absence of self-adaptation on situation awareness process, a Network Security Situation Awareness Model （NSSAM） based on autonomic computing was proposed. The situation extraction was analyzed in real-time by an autonomic feedback law. From the perspectives of attack and defense, a multi-level and multi-angle network security situation assessment model employing Analytic Hierarchy Process （AHP） was established according to the extracted situation information. The model of future network security situation prediction adopting improved genetic neural network was built on the basis of the past and current network security situation. Test results show that NSSAM with autonomic feedback mechanism can effectively enhance self-adaptation of the system.
出处
《计算机应用》
CSCD
北大核心
2013年第2期404-407,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61003035
61142002
U1204614)
河南省高等学校青年骨干教师资助计划项目(2009GGJS-050)
河南省科技攻关项目(112102210187)
关键词
自律计算
网络安全态势感知
态势提取
态势评估
态势预测
autonomic computing
network security situation awareness
situation extraction
situation assessment
situation prediction
