基于亲和力阈值的静态克隆选择算法

静态克隆选择算法用于产生检测特定“非我”的检测器。该文通过引入亲和力阈值参数改进静态克隆选择算法,使其匹配规则能够灵活表示“或”关系,且只须部分匹配即可高效提取能识别“非我”的部分样本特征。自我/非我区别的模拟实验结果表明,与静态克隆选择算法相比,该算法能更有效地产生部分分类规则。     

基于Monte Carlo估计的免疫检测器分布优化算法

针对免疫实值检测器的黑洞和边界入侵问题,分析规模对检测性能的影响,提出一种基于Monte Carlo估计的检测器分布优化算法,以Monte Carlo方法估计检测器对非自体空间的覆盖效果作为算法结束的条件,通过优秀子代替代不合时宜的父代来完成检测器的分布优化处理。经实验测试表明,该算法不仅可以有效地降低黑洞,而且能够以更少的检测器更精确地覆盖非自体空间,从而提升检测器的检测性能。     

基于反向选择的地震前兆观测数据异常检测

针对现有前兆异常检测方法因异常数据较少导致检测准确率偏低的问题,提出一种基于反向选择的检测方法。定义地震数据中的self集与nonself集;将随机选取的未成熟检测器与self集进行匹配,生成半径可变的成熟检测器,覆盖nonself空间;将待检测数据与检测器匹配,通过判断是否在nonself空间得到检测结果;与现有地震异常检测方法BP神经网络、支持向量机进行对比,实验结果表明反向选择用于地震前兆观测数据异常检测有更好的效果。     

免疫算法在模糊检测器生成中的应用

目前入侵检测中传统否定选择算法忽略了正常和异常模式之间的模糊界限而造成了检测效率低下,以及生成的检测器数量冗繁,用在非我模式识别时计算复杂度相当高.针对这些缺陷,重点研究了在入侵检测系统中定义模糊检测规则的重要性,并提出利用免疫算法的优化搜索性能来进化模糊检测器的方法.实验结果表明,该方法生成的检测器能够允许更简洁的自我和非我的表示方式,降低了检测规则的脆弱性,检测效果较好.     

基于协同进化的免疫检测器分布优化算法

为解决免疫实值检测器的黑洞问题,分析检测器规模对检测性能的影响,提出一种基于协同进化的免疫实值检测器分布优化算法。将检测器集分成不同子集,寻找每个子集的最优个体,利用各子集问的相互作用与影响对各子集进行优化处理,取并集构成完整检测器集。实验结果表明,与否定选择算法相比,该算法不仅可以有效减少黑洞的产生,并且能以较少的检测器精确地覆盖非自体空间,从而提高检测器性能。     

一种基于受体编辑的实值阴性选择算法

受生物免疫受体编辑理论的启发,提出了一种基于受体编辑的实值阴性选择算法RERNS(Receptor Editinginspired Real Negative Selection Algorithm).对于匹配自体的检测器,该算法采用定向受体编辑使之获得新生,而这些新生的检测器分布在自体与非自体的边界区域,从而增加了检测器的多样性,并改善了算法对边界区域的覆盖情况;对于不匹配自体的检测器,该算法采用识别相同最近自体的定向受体编辑,使检测器在包含原检测范围的情况下扩大了对非自体空间的覆盖.理论分析和实验验证表明,与实值阴性选择算法中具有代表性的RNS算法和V-detector算法相比,RERNS算法生成的未成熟检测器更少,且检测性能更好.     

基于免疫的最小有效检测器集生成算法

通过对可变模糊匹配阴性选择算法生成的检测器集覆盖空间的分析,针对检测器集中检测器之间出现的互相匹配的现象,提出一种最小有效检测器集生成算法。该算法利用模糊思想确定匹配阈值,有效去除了检测器集中的冗余现象。仿真结果表明,由该算法所生成的检测器集空间覆盖率显著提高,实现了以较小的检测器集合,检测到较大范围的“非己”行为,从而提高了系统的检索率和性能。     

基于免疫的入侵检测模型中空洞的分析及对策

根据人工免疫的原理、体系结构,建立了一种新的基于免疫原理的分布式网络入侵检测系统模型。该模型中存在着检测子集合无法检测到的非我--"空洞"。"空洞"会导致模型性能的下降,漏报率的增高。在详细分析了"空洞"产生的原因以及"空洞"的相关特性后,给出了减少 "空洞"的对策,并用模拟试验的方式验证了不同形状的检测子可以有效弥补"空洞",从而使系统的漏报率下降。     

一种采用混合搜索的检测器生成算法

检测器生成算法中采用随机搜索生成的检测器会产生大量重叠,而采用进化搜索收敛速度较慢.将两种搜索方式相结合,提出一种采用混合搜索的检测器生成算法,该算法将随机搜索产生的检测器集作为进化搜索的初始种群,使用遗传算法进化产生成熟检测器.使用二雏人工数据测试算法.结果表明该算法能够以更少的检测器更精确地覆盖非自体空间,并能提升收敛速度.     

一种采用边界检测器的实值否定选择算法

针对实值否定选择算法中由边界困境问题引发的在自体与非自体区域边界产生漏洞的现象,提出了一种采用边界检测器的实值否定选择算法.该算法在边界上生成具有一定侵略性的边界检测器,通过边界阚值控制的边界检测器不仅能够有效地减少边界上的漏洞,还能探明自体与非自体区域边界.使用人工数据和MIT Darpa 1998离线数据对算法进行了测试,结果表明尽管新方法具有较高的最小误报率,但在误报率相同的情况下,有更高的检测率.     

基于拟随机序列与克隆选择的进化V-detector 算法

阴性选择(NS)算法是人工免疫的核心方法,检测器生成是其关键。针对其经典V-detector算法中高维数据失效及随机生成初始检测器集过于集中而导致过早收敛等问题,首先采用拟随机序列生成初始检测器;然后通过克隆选择优化检测器集合,以覆盖非自体空间大小及数量作为亲和力标准,克服传统进化阴性选择(ENS)算法的局限性,并采用新型进化算子使得算法生成最优检测器集合;最后,通过实验验证了该方法的有效性。     

V-detector: An efficient negative selection algorithm with “probably adequate” detector coverage

This paper describes an enhanced negative selection algorithm (NSA) called V-detector. Several key characteristics make this method a state-of-the-art advance in the decade-old NSA. First, individual-specific size (or matching threshold) of the detectors is utilized to maximize the anomaly coverage at little extra cost. Second, statistical estimation is integrated in the detector generation algorithm so the target coverage can be achieved with given probability. Furthermore, this algorithm is presented in a generic form based on the abstract concepts of data points and matching threshold. Hence it can be extended from the current real-valued implementation to other problem space with different distance measure, data/detector representation schemes, etc. By using one-shot process to generate the detector set, this algorithm is more efficient than strongly evolutionary approaches. It also includes the option to interpret the training data as a whole so the boundary between the self and nonself areas can be detected more distinctly. The discussion is focused on the features attributed to negative selection algorithms instead of combination with other strategies.     

检测器自适应生成算法研究

如何有效生成检测器是用于异常检测的非选择算法的核心问题,也是非选择算法能否实际应用的关键问题.本文提出了一种有效的检测器自适应生成算法,能够依据实际情况不断调整当前检测器集合,在使得仅用较小的检测器集就能够快速检测到大规模非我空间中的异常变化的同时,也保证了算法的普适性,对各种异常检测问题具有一定的适用性.文中对算法的理论基础进行了分析,给出了算法的实现范例和实验结果.实验结果表明了算法的有效性.     

一种检测器长度可变的非选择算法

检测器生成是非选择算法的关键步骤.已有检测器生成算法在生成检测器时存在"漏洞"区域和冗余检测器问题.提出了一种检测器长度可变的检测器生成算法,不仅可以消除"漏洞"区域,还可以通过相应的检测器优化算法减少冗余检测器,进而提高检测器生成效率和检测效率.对算法进行了分析和实验证明,结果表明,该算法比传统的非选择算法及r可变的非选择算法具有更好的性能.     

Improved email spam detection model with negative selection algorithm and particle swarm optimization

The adaptive nature of unsolicited email by the use of huge mailing tools prompts the need for spam detection. Implementation of different spam detection methods based on machine learning techniques was proposed to solve the problem of numerous email spam ravaging the system. Previous algorithm used in email spam detection compares each email message with spam and non-spam data before generating detectors while our proposed system inspired by the artificial immune system model with the adaptive nature of negative selection algorithm uses special features to generate detectors to cover the spam space. To cope with the trend of email spam, a novel model that improves the random generation of a detector in negative selection algorithm (NSA) with the use of stochastic distribution to model the data point using particle swarm optimization (PSO) was implemented. Local outlier factor is introduced as the fitness function to determine the local best (Pbest) of the candidate detector that gives the optimum solution. Distance measure is employed to enhance the distinctiveness between the non-spam and spam candidate detector. The detector generation process was terminated when the expected spam coverage is reached. The theoretical analysis and the experimental result show that the detection rate of NSA–PSO is higher than the standard negative selection algorithm. Accuracy for 2000 generated detectors with threshold value of 0.4 was compared. Negative selection algorithm is 68.86% and the proposed hybrid negative selection algorithm with particle swarm optimization is 91.22%.     

Collaborative RFID intrusion detection with an artificial immune system

The current RFID systems are fragile to external attacks, due to the limitations of encryption authentication and physical protection methods used in implementation of RFID security systems. In this paper, we propose a collaborative RFID intrusion detection method that is based on an artificial immune system (AIS). The new method can enhance the security of RFID systems without need to amend the existing technical standards of RFID. Mimicking the immune cell collaboration in biological immune systems, RFID operations are defined as self and nonself antigens, representing legal and illegal RFID operations, respectively. Data models are defined for antigens’ epitopes. Known RFID attacks are defined as danger signals represented by nonself antigens. We propose a method to collect RFID data for antigens and danger signals. With the antigen and danger signal data available, we use a negative selection algorithm to generate adaptive detectors for self antigens as RFID legal operations. We use an immune based clustering algorithm aiNet to generate collaborative detectors for danger signals of RFID intrusions. Simulation results have shown that the new RFID intrusion detection method has effectively reduced the false detection rate. The detection rate on known types of attacks was 98% and the detection rate on unknown type of attacks was 93%.     

邻域形态空间多源免疫检测器生成与检测

人工免疫系统（artificial immune system,简称AIS）是人工智能技术的重要分支之一,被广泛应用于异常检测、数据挖掘、机器学习等多个领域.检测器是其核心知识集,其生成、优化和检测操作决定了人工免疫的应用效果.目前,人工免疫的问题空间以实值形态空间为主,但实值非自体空间“黑洞”、检测器生成速率慢、检测器高重叠冗余、“维度灾难”等问题,使得人工免疫检测的效果不甚理想.鉴于此,使用邻域形态空间,并改进邻域否定选择算法（neighborhood negative selection algorithm,简称NNSA）,引入混沌理论和遗传算法,提出了一种多源邻域否定选择算法（multi-source-inspired NNSA,简称MSNNSA）,并基于此提出邻域形态空间多源免疫检测器生成与检测方法,改进邻域形态空间下检测器的构造与生成机制,使其更具靶向性,并使获得的检测器具有更好的分布性,提高其生成效率和整体的检测性能,解决以上实值形态空间下存在的问题.实验结果表明,该方法提高了检测器生成效率以及检测的整体性能和稳定性.     

A neural networks-based negative selection algorithm in fault diagnosis

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA.     

改进的否定选择算法在入侵检测中的应用

针对基于否定选择算法入侵检测系统检测率低的问题,提出一种新的入侵检测算法,着重分析了检测器生成模块,对原否定选择算法做出了改进。改进算法主要对采用了基于空间包含的匹配算法和B、T双检测器来进行检测,增强检测器的多样性,提高了入侵检测系统的检测能力。最后通过实验证明,改进的否定选择算法提高了入侵检测系统的检测率。     

自适应检测器生成算法研究

基于小生境策略的否定选择算法利用在搜索空间中计算检测器之间的海明距离,构建小生境;一个与亲合力函数相关的适应度函数的提出,能更客观地反映各检测器的匹配能力,即能更准确地反映检测器集合的检测能力;利用进化策略,进行遗传操作,而生成多样性和通用性的最佳检测器集.同时该算法可以减少生成检测器的时间开销.