BIORV-NSA: Bidirectional inhibition optimization r-variable negative selection algorithm and its application

The original negative selection algorithm (NSA) has the disadvantages that many “black holes” cannot be detected and excessive invalid detectors are generated. To overcome its defects, this paper improves the detection performance of NSA and presents a kind of bidirectional inhibition optimization r-variable negative selection algorithm (BIORV-NSA). The proposed algorithm includes self set edge inhibition strategy and detector self-inhibition strategy. Self set edge inhibition strategy defines a generalized radius for self individual area, making self individual radius dynamically be variable. To a certain extent, the critical antigens close to self individual area are recognized and more non-self space is covered. Detector self-inhibition strategy, aiming at mutual cross-coverage among mature detectors, eliminates those detectors that are recognized by other mature detectors and avoids the production of excessive invalid detectors. Experiments on artificially generating data set and two standard real-world data sets from UCI are made to verify the performance of BIORV-NSA, by comparison with NSA and R-NSA, the experimental results demonstrate that the proposed BIORV-NSA algorithm can cover more non-self space, greatly improve the detection rates and obtain better detection performance by using fewer mature detectors.     

基于免疫原理的计算机病毒检测研究应用

针对计算机病毒新品种及已知病毒新变种的难以检测问题,该文提出了根据生物免疫原理检测未知病毒的构想．并建立了计算机病毒检测模型,重点讨论了部分算法及技术,最后分析其有效性。     

基于免疫原理的计算机病毒检测研究应用

针对计算机病毒新品种及已知病毒新变种的难以检测问题,该文提出了根据生物免疫原理检测未知病毒的构想,并建立了计算机病毒检测模型,重点讨论了部分算法及技术,最后分析其有效性。     

一种关键任务系统自律可信性模型与量化分析

将现有入侵容忍、自毁技术与自律计算相结合,提出了一种基于SM-PEPA(semi-Markov performance evaluation process algebra)的关键任务系统自律可信性模型以支持形式化分析和推理.该模型具有一定程度的自管理能力,采用分级处理的方式应对各种程度的可信性威胁,满足了关键任务系统对可信性的特殊需求.在此基础上,从稳态概率角度提出了一种自律可信性度量方法.最后,结合具体实例对模型参数对自律可信性的影响进行了初步分析.实验结果表明,增大关键任务系统可信性威胁检测率和自恢复成功率,可在较大范围内提高系统的自律可信 特性.