人工免疫系统:原理、模型、分析及展望

目前，受生物免疫系统启发而产生的人工免疫系统（Artificial ImmuneSystem,AIS)正在兴起，它作为计算智能研究的新领域，提供了一种强大的信息处理和问题求解范式，该文侧重以AIS的基本原理框架为线索，对其研究状况加以系统综述，首先从AIS的生物原型入手，归纳提炼出其仿生机理，主要包括免疫识别，免疫学习，免疫记忆，克隆选择，个体多样性，分布式和自适应等，进而对几种典型的AIS模型和算法分门别类地进行了细致讨论，随后介绍了AIS在若干具有代表性的领域中的应用情况，最后通过对AIS的特性和存在问题的分析，展望了今后的研究重点和发展趋势。  

Artificial immune systems as a novel soft computing paradigm

Artificial immune systems (AIS) can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. Their development and application domains follow those of soft computing paradigms such as artificial neural networks (ANN), evolutionary algorithms (EA) and fuzzy systems (FS). Despite some isolated efforts, the field of AIS still lacks an adequate framework for design, interpretation and application. This paper proposes one such framework, discusses the suitability of AIS as a novel soft computing paradigm and reviews those works from the literature that integrate AIS with other approaches, focusing ANN, EA and FS. Similarities and differences between AIS and each of the other approaches are outlined. New trends on how to create hybrids of these paradigms and what could be the benefits of this hybridization are also presented. Leandro N. de Castro would like to thank the Computing Laboratory and CNPq (Profix n. 540396/01-0) for the financial support and Prof. Dr. Fernando J. Von Zuben for his indispensable comments on the development of a framework for the AIS. Jon Timmis would like to thank the Computing Laboratory, UKC for their continued support in this new area of research.  

进化多目标优化算法研究

进化多目标优化主要研究如何利用进化计算方法求解多目标优化问题,已经成为进化计算领域的研究热点之一.在简要总结2003年以前的主要算法后,着重对进化多目标优化的最新进展进行了详细讨论.归纳出当前多目标优化的研究趋势,一方面,粒子群优化、人工免疫系统、分布估计算法等越来越多的进化范例被引入多目标优化领域,一些新颖的受自然系统启发的多目标优化算法相继提出;另一方面,为了更有效的求解高维多目标优化问题,一些区别于传统Pareto占优的新型占优机制相继涌现;同时,对多目标优化问题本身性质的研究也在逐步深入.对公认的代表性算法进行了实验对比.最后,对进化多目标优化的进一步发展提出了自己的看法.  

免疫机制在计算机网络入侵检测中的应用研究

首先对当前入侵检测中所应用的免疫机制进行了简要的介绍；接着，对迄今所提出的3个基于免疫机制的入侵检测模型做了较为详细的描述和分析；最后，根据在免疫机制研究中的一些体验，提出了一些值得研究的机制和方向，以及对这项研究的一些认识。  

人工免疫原理在网络入侵检测中的应用

分析研究了人工免疫原理在网络入侵检测中应用的可行性，结合人工免疫模型和数据挖掘技术建立了一个网络入侵检测系统模型对抗体生成过程中的关键算法进行了描述。  

基于免疫的网络监控模型

传统计算机免疫系统检测器训练效率低下，自体／非自体定义缺乏动态的演化机制，适应性较差，不能满足真实网络环境下网络监控的需求．有鉴于此，该文提出了一种新型的基于免疫的网络监控模型（AINM），给出了自体、非自体、抗原、检测器以及证据的形式定义，建立了自体、抗原、动态取证、免疫耐受、检测器的生命周期以及免疫记忆的动态模型及相应的递推方程，并对模型进行了仿真．实验表明这种新型的网络监控模型具有良好的多样性、实时性和自适应能力．  

Anomaly Detection Using Real-Valued Negative Selection

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.  

改进人工免疫算法的分析研究

详细分析了人工免疫算法(AIA)基于信息熵计算抗体浓度存在的缺点，提出了三种新的浓度计算方法。分析了基于新的浓度计算方法的各种改进的人工免疫算法的收敛性。时两种变形大海捞针类函数的优化结果表明，改进的人工免疫算法较传统的人工免疫算法有较大的优越性。  

An immunity based network security risk estimation

There are two kinds of risk-estimation methods for the network security: static and real-time. The static methods estimate the network risk through statically evaluating the network value, security holes, and the occurring frequency of security events[1], e.g., COBRA1), OCTAVE[2], etc. Focusing on the static factors of the target system, the static methods can only make a rough estimation of the security risk that the network faced in the past, and cannot in real-time evaluate the risk of …  

人工免疫算法及其应用研究

为了有效地解决病态的约束优化问题,提出了一种模拟生物免疫系统自我调节功能的人工免疫算法,介绍了算法的基本步骤,构造了几种人工免疫算子,分析了算法的收敛性.人工免疫算法继承了遗传算法“优胜劣汰”的自我淘汰机制,但新抗体的产生方法比遗传算法中新个体的产生方法灵活得多.在进行抗体选择时若能确保当时的最优抗体可以进入下一代抗体群,则人工免疫算法是全局收敛的.100个城市TSP问题的仿真实例显示人工免疫算法比遗传算法具有更强的全局搜索能力和收敛速度.