邻域形态空间与检测算法

针对免疫入侵检测中实值空间存在的问题,借助免疫细胞的表位组织形式和离散拓扑理论,提出一种新的形态空间表示法—–邻域表示法.该方法利用数据的集合特性,采用空间中互不相交的邻域表示自体/检测器,并设计匹配策略和检测算法.实验表明,邻域空间可以较好地弥补实值空间的缺陷,提高检测器生成效率,改善系统整体检测效果.     

一种可变阈值检测器生成算法的研究

在构造人工免疫系统中,如果能自动调节生成检测器的大小,从而利用较少的检测器,实现对较大"非自体集"的检测,就可从根本上提高系统效率.本文通过分析人工免疫系统中的主要算法--否定选择算法,以及检测器产生漏洞的原因,提出了一种可变阈值的检测器生成算法.与传统的否定选择算法相比,该算法大大减少了漏洞,使检测效率得到提高.     

基于局部线性嵌入的免疫检测器优化生成算法

网络安全已上升到国家安全战略层面,入侵检测技术是其重要的组成部分,已得到广泛关注.在基于免疫的入侵检测研究中,针对传统实值否定选择算法不利于高效分析数据而造成的检测器生成速度慢、检测效率低等问题,引入局部线性嵌入算法,借鉴其能对高维数据进行映射降维的特点,提出一种基于局部线性嵌入的免疫检测器优化生成算法,利用局部线性嵌入对高维数据预处理优化降维,并结合实值否定选择算法生成检测器.将该算法用于检测模型,从而提升检测器的生成速率,并可保证生成的检测器高效地处理高维数据.该算法在降维前后可保证样本的局部线性结构不变,具有可变参数少、计算时间短的特点.实验结果表明,所提出算法在显著提高检测器生成速率和对数据检测效率的基础上,检测性能也表现出很好的水平.     

实值n维混沌映射否定选择算法

针对传统的基于二进制的混沌否定选择算法在检测器生成阶段对混沌映射产生的混沌序列离散化生成的候选检测器,不利于知识和数据的分析,也会造成检测器集生成速度慢及检测效率低等问题,提出了基于实值的混沌否定选择算法.引入混沌理论,采用混沌特性更好的自映射构造n维混沌映射生成候选检测器中心点,改进了传统的检测器生成机制,更适合处理高维空间问题;对原有的V-detector算法进行了优化,通过定向移动与计算几何中心相结合的思想确定检测半径.旨在满足预期覆盖率条件下尽量使半径取值最大化,扩大检测器集的覆盖范围,减少检测器数量.实验结果表明,该算法提高了检测器集的生成速度和检测效率.     

一种改进的否定选择算法在入侵检测中的应用

有效的检测器生成算法是入侵检测的核心问题。针对现有算法存在检测率低、匹配阈值固定、检测器集合庞大等问题,通过对人工免疫系统中否定选择算法原理的分析,提出一种生成最有效检测器集的变阈值模糊匹配否定选择免疫算法,并将该算法应用到入侵检测系统中。算法采用随机生成和基因库相结合的候选检测器生成机制,在保证检测器多样性的同时,提高了候选检测器成为成熟检测器的比率。为了消除冗余检测器的产生,提高检测器集的检测效率,算法在模糊匹配的基础上生成有效检测器集。同时,匹配阈值可变,可大幅降低黑洞数量。实验结果表明,该算法提高了入侵检测率,降低了虚警率,整体检测性能较好。     

快速否定选择算法的研究与分析

人工免疫算法具有良好的特性,已被广泛应用于入侵检测、信息恢复、敷据挖掘等领域的研究中,否定选择算法是人工免疫算法中的典型算法,但存在重复检查、检测器查找效率低及逐位比较的时间和空间开销大等问题.我们分析否定选择算法中匹配算法的特点,设计自体、检测器和抗原中检测元素的转换算法,提取自体数、检测数和待检数,引入红黑树建立索引.设计基于红黑树的快速否定选择算法,避免反复提取子串和重复比较寻问题,提高检测效率,最后实现了快速否定选择算法的原型系统,测试、比较了否定选择算法和基于红黑树快速否定选择算法的检测效率,洲试结果表明使用基于红黑树快速否定选择算法检测抗原,能有效的减少比较次数,提高检测效率.     

否定选择算法在网络入侵检测中改进应用

入侵检测最主要需要解决的问题就是检测器生成算法,然而,当前的算法存在着一些弊端不能很好的解决入侵检测问题,本文基于此在对人工免疫系统中否定选择算法进行研究的基础上,对该否定选择算法在网络如今检测中的改进应用进行研究,并通过实验结果证明,对这种否定选择算法在网络入侵检测中的改进应用,确实提高了提高了入侵检测率,降低了虚警率,表现出较好的整体检测性能。     

一种基于多级否定选择的入侵检测器生成算法

文中给出一种改进的基于人工免疫入侵检测系统的否定选择算法。首先是用多级否定选择算法生成不同检测尺度的成熟检测器,然后为了模仿人体免疫系统中的第二次应答机制,引入了记忆检测器的概念及相应的算法,结合亲和力成熟与体细胞突变等方法,将成熟检测器提升为识别率极高的记忆检测器。     

线性时间检测器生成算法的改进

针对基于人工免疫的网络入侵检测中的线性时间检测器生成算法生成的检测器存在冗余以及时问和空间代价与r呈指数关系，算法开销受r的影响较大的不足，对其进行了改进，采用了去除冗余检测器以及分割字符串的改进算法来生成检测器，通过实验证明了算法的有效性。     

一种采用混合检测器的入侵检测系统

传统的基于免疫的入侵检测系统采用低级别的二进制检测器,妨碍了有意义的知识提取,对Nonself空间的覆盖也不完备。对二进制Self集的确定和有效检测器的生成方法进行了改进,研究了实值否定选择算法,加入了实值检测器,构成混合检测器集合,在检测阶段对会话和数据包同时进行异常检测。实验结果ROC曲线表明有较高的检测率和较低的误报率。     

基于免疫的检测漏洞问题研究

针对已有实值非选择算法中检测漏洞问题,提出一种改进的算法提高对检测漏洞的覆盖。算法基于可变长实值检测器实现,主要思想是把自体样本分为边界自体样本和非边界自体样本。在检测器的生成过程中,鉴别和记录边界自体样本;在对新样本的检测过程中,检测是否匹配边界自体。通过人工合成数据集2DSyntheticData和实际Iris 数据集对算法进行了验证。实验结果表明,算法检测率较高,在覆盖自体和非自体边界处的漏洞方面明显优于已有的算法。     

超球体检测器覆盖问题的研究

研究表明实值否定选择算法在多维形状空间下呈现出很高的时间和空间复杂性。针对实值否定选择算法中最常采用的超球体检测器,在理论上研究了它的体积,以及体积随半径和维数变化的性质,以此分析了高复杂性出现的原因。针对检测器存在重叠的问题,基于蒙特卡罗方法提出了一个估计检测器覆盖率的算法,用于比较不同检测器生成算法。由于该算法基于随机分布和概率方法,它极大地简化了计算复杂性。     

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%.     

Using known nonself samples to improve negative selection algorithm

Negative selection algorithm is the core algorithm of artificial immune system. It only uses the self for training and generates detectors to detect abnormalities. Holes are feature space areas that the detector fails to cover, it is the root cause of the performance degradation of the negative selection algorithm. The conventional method generates a large number of detectors randomly to repair the holes, which is time-consuming and not effective. To alleviate the problem, we propose a V-Detector-KN algorithm in this paper. V-Detector is the abbreviation of the real-valued negative selection algorithm with Variable-sized Detectors, KN represents Known Nonself. The V-Detector-KN algorithm uses the known nonself as the candidate detector to further generate the detector based on the V-Detector randomly generated detector, so as to realize the repair of holes. Compared with the conventional method to randomly generate detectors to repair holes, our proposed V-Detector-KN method uses known nonself to repair holes, reducing the randomness and blindness of hole repair. Theoretical analysis shows that the detection rate of our algorithm is not lower than that of the conventional V-Detector algorithm. The results of experiment comparing with other 6 algorithms on 7 UCI data sets show the superiority of our proposed algorithm.

     

Intelligent multi-user detection using an artificial immune system

Artificial immune systems (AIS) are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multiple-access communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.     

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm (NSA) is one of the classic artificial immune algorithm widely used in anomaly detection. However, there are still unsolved shortcomings of NSA that limit its further applications. For example, the nonself-detector generation efficiency is low; a large number of nonself-detector is needed for precise detection; low detection rate with various application data sets. Aiming at those problems, a novel radius adaptive based on center-optimized hybrid detector generation algorithm (RACO-HDG) is put forward. To our best knowledge, radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity. RACO-HDG works efficiently in three phases. At first, a small number of self-detectors are generated, different from typical NSAs with a large number of self-sample are generated. Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible. Secondly, without any prior knowledge of the data sets or manual setting, the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism. In this way, the number of abnormal detectors is decreased sharply, while the coverage area of the nonself-detector is increased otherwise, leading to higher detection performances of RACO-HDG. Finally, hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected. Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate, lower false alarm rate and higher detection efficiency compared with other excellent algorithms.      

An antigen space density based real-value negative selection algorithm

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an “antibody suppression rate” to replace the “expected coverage” as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs.