树突细胞算法及其理论研究

树突细胞算法(DCA)是受先天性免疫系统中树突细胞(DCs)功能的启发而开发的算法,它已被成功运用于许多计算机安全相关领域。但是对DCA理论方面的分析工作很少,对算法大多数理论方面的研究也较少出现。而其它的人工免疫算法如负选择算法、克隆选择算法在理论方面的研究工作却出现在很多文献中。因此对DCA算法进行相似的理论分析,确定算法的运行时间复杂度,揭示其它算法的属性就显得非常重要。根据算法执行的3个阶段,通过引入3个运行时间变量实现对DCA算法的理论分析。标准DCA算法取得的运行时间复杂度下界为Ω(n),而在最坏情况下的时间复杂度为O(n2)。另外,如果利用"分片"方法实现DCA的在线分析组件,则算法的运行时间复杂度可以改进为O(max(nN,nδ))。     

DCA自动数据预处理技术研究

树突细胞算法（DCA）能够在数据规模方面有效地处理大数据集。然而,在处理复杂数据集时,数据规模不是唯一需要考虑的,也要考虑高维数据问题。树突细胞算法的复杂性出现在数据预处理阶段,因此数据降维就尤其重要,以往,树突细胞算法的数据预处理是根据问题域的专家知识采用手工方法执行的,既浪费时间又是难以实现的。提出利用主成分分析法实现DCA的自动数据预处理,提取和选择相关特征使算法适应于基础数据的特点。在KDDCUP’99 数据集上将PCA应用于DCA显示其可行性,并产生有用且准确的分类结果。     

基于人工免疫系统的入侵检测研究

人工免疫系统（AIS）作为解决入侵检测问题的一种方法,已经显示其突出的优点并得到快速发展。为使入侵检测系统的研究者更进一步了解基于AIS的入侵检测研究进展,回顾基于第1代和第2代AIS的入侵检测常用算法,并指出算法特点。阐述树突细胞算法（DCA）适合于解决入侵检测问题的优势,给出针对DCA算法的未来研究工作,包括该算法的形式化描述、通过分片思想实现DCA在线分析组件以及DCA输入数据的自动数据预处理。     

基于树突细胞算法与对支持向量机的入侵检测

针对入侵检测技术在处理大规模数据时存在的高误报率、低训练速度和低实时性的问题,提出了一种基于树突细胞算法与对支持向量机的入侵检测策略(DCTWSVM).利用树突细胞算法(DCA)对威胁数据进行初始检测,在此基础上利用对支持向量机(TWSVM)进行检测结果的优化处理.为了验证策略的有效性,设计性能对比实验,实验结果表明,相较于DCA、支持向量机(SVM)、反向传播(BP)神经网络,DCTWSVM策略的检测精度提高了2.02%、2.30%、5.44%,误报率分别降低了0.26%、0.46%、0.90%,训练速度相较于SVM提高了两倍且只需耗费极少的训练时间,可以更好地适用于大规模数据下的实时入侵检测环境.     

结合XGBoost的树突状细胞改进算法

树突状细胞算法(DCA)要求输入3类信号,需要通过人工选取或统计学等方式提前进行特征提取。为准确、高效地提取特征,提出一种基于XGBoost的DCA。通过使用XGBoost算法迭代生成决策树,根据决策树的特征节点对数据集的特征指标进行提取与分类,并作为DCA的信号输入以实现算法优化。使用KDD99数据集进行实验,结果表明,与基于粗糙集的改进算法相比,该算法的准确率更高,最高可达0.859 00。     

一种改进的树突状细胞算法及其性能稳定性实验

针对原有树突状细胞算法DCA(Dendritic Cell Algorithm)在环境状态转变时存在误分类,且随着状态转变的次数越多错误率越高的问题,在原算法的基础上通过设置动态阈值,加入后续抗原对当前采样抗原的评价因子,以及具有放大其他信号功能的发炎信号等策略对其进行改进,将改进算法应用于标准数据集Breast Cancer。实验结果表明,与原DCA算法相比,该算法的稳定性和识别率都有一定改善。     

树突状细胞算法原理及其应用

描述树突状细胞的生物学机理,给出树突状细胞算法(DCA)的设计、实现及改进过程。介绍DCA在计算机网络、无线传感器网络、实时嵌入式系统和机器人学等方面的应用情况,对算法性能进行评价。基于标准数据集,将DCA与其他方法进行比较,分析DCA存在的问题,指出其下一步的研究方向。     

基于粗糙集的改进树突状细胞算法

树突状细胞算法（DCA）在应用于入侵检测时,需要对网络监测数据进行约简,以降低系统负担,提高检测效率。提出一种结合粗糙集属性约简和DCA的异常入侵检测方法。采用粗糙集属性重要度对数据集进行属性约简,产生DCA输入信号,而后利用DCA算法进行入侵检测。通过KDD CUP99数据集对所提出的改进算法进行验证,结果表明,算法在保证检测率的前提下,显著降低了误报率。算法实现了入侵检测特征的自动提取,显著减少了所需检测的特征数目,加快了算法运行速度,具有良好的综合性能。     

带树突的多层形态感知器的交叉覆盖设计算法

带树突结构的形态感知器是用格代数的方法对样本集进行分类的。树突结构目前被认为是实现逻辑运算最基本的自主计算单元,引入树突计算过程的神经元也就更接近于实际的生物神经元,具有很强的计算能力。论文根据树突过程的几何意义,通过用树突输出的方框代替覆盖算法中的“球形领域”,提出一种适于多层形态感知器学习的算法,叫做形态交叉覆盖算法,并在此基础上给出一种带树突结构的多层形态感知器模型。三个著名分类问题的实验结果很好地证明了该模型的有效性。     

Android恶意软件的人工自然杀伤细胞检测模型

针对现有Android恶意软件检测方法中存在的特征分析单一和固定化、对未知和潜伏性强的恶意软件检测能力弱等问题,构建一种Android恶意软件的人工自然杀伤细胞（Natural Killer cell,NK）检测模型。对人工自然杀伤细胞模型和树突状细胞算法（Dendritic Cell Algorithm,DCA）进行了研究,结合软件静态权限申请特征和动态API调用特征,经数据预处理后形成模型的各类输入信号。人工NK细胞输出刺激因子与DCA危险信号融合,提高了DCA的危险信号显著性,优化了DCA的检测过程。实验包含从VirusTotal等数据集选取的多种分类恶意软件样本1 150个,良性软件样本1 093个。实验结果表明与DCA和[K]-means等检测方法相比人工NK细胞检测模型提高了准确率并且降低了误报率。     

An immune optimization based deterministic dendritic cell algorithm

Anomaly detection is an important issue, which has been deeply studied in different research domains and application fields. The dendritic cell algorithm (DCA) is one of the most popular artificial immune system inspired approaches to handle anomaly detection problems. The performance of DCA depends significantly on the parameters used to compute the relationship between input instance and detectors. However, we find that while the DCA’s performance is good in practical applications, it is difficult to analyze due to the empirical based parameters and lacks adaptability. This paper studies how to effectively learn appropriate parameters for deterministic DCA (dDCA) for anomaly detection tasks. In particular, we propose a novel immune optimization based dDCA (IO-dDCA) for anomaly detection. It consists of dDCA classification, T cell (TC) classification, gradient descent optimization and immune nonlinear dynamic optimization. First, the dDCA is regarded as a binary classifier, and the data instances which are labeled as normal will be classified by a T cell inspired classification method, so as to improve the classification performance of dDCA. Then, to improve dDCA’s adaptability, gradient descent is adopted for dDCA parameters’ optimization. Finally, the immune nonlinear model is introduced to adjust learning rate in gradient descent to find the optimal parameters. The theoretical and experimental performance analysis of IO-dDCA show effectiveness of the novel approach through simulations, and the experimental results show that the proposed IO-dDCA has good classification accuracy.

     

人工免疫网络记忆分类并行算法研究

人类免疫系统是极为复杂的、固有并行性、分布式系统。人工免疫系统领域已经开发了许多免疫系统启发的算法,但没有几个显示并行性。论文提出并行人工免疫网络记忆分类系统,给出简单的并行人工免疫网络记忆分类算法。初始结果显示,通过简单的并行化方法,与并行人工免疫识别系统AIRS的比较研究表明,并行人工免疫网络记忆分类系统在并行效率等方面的性能优于后者。     

Artificial immune algorithm for IIR filter design

Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse response (IIR) digital filters since these algorithms have the ability of finding global optimal solution in a nonlinear search space. One of the modern heuristic algorithms is the artificial immune algorithm which implements a learning technique inspired by human immune system. However, the immune system has not attracted the same kind of interest from researchers as other heuristic algorithms. In this work, an artificial immune algorithm is described and applied to the design of IIR filters, and its performance is compared to that of genetic and touring ant colony optimisation algorithms.     

A fast and efficient clustering based fuzzy time series algorithm (FEFTS) for regression and classification

Forecasting fuzzy time series (FTS) methods are generally divided into two categories, one is based on intervals of universal set and the other is based on clustering algorithms. Since there are some challenging problems with the interval based algorithms such as the ideal interval length, clustering based FTS algorithms are preferred. Fuzzy Logical Relationships (FLRs) are usually used to establish relationships between input and output data in both interval based and clustering based FTS algorithms. Modeling complicated systems demands high number of FLRs that incurs high runtime to train FTS algorithms. In this study, a fast and efficient clustering based fuzzy time series algorithm (FEFTS) is introduced to handle the regression, and classification problems. Superiority of FEFTS algorithm over other FTS algorithms in terms of runtime and training and testing errors is confirmed by applying the algorithm to various benchmark datasets available on the web. It is shown that FEFTS reduces testing RMSE for regression data up to 40% with the least runtime. Also, FEFTS with the same accuracy as compared to Fuzzy-Firefly classification method, diminishes runtime moderately from 324.33 s to 0.0055 s.     

基于危险理论和DCA的WSNs入侵检测系统模型

针对无线传感器网络(WSNs)由于自身特点易于遭到入侵且传统被动的安全机制无法完全应对这一问题,对人工免疫系统(AIS)进行研究,设计一种新的入侵检测系统(IDS)模型。模型采用危险理论和适用于WSNs的改良树突状细胞算法(DCA),可使节点之间彼此分工合作共同识别入侵,加强了网络的鲁棒性。仿真结果显示:与早期的自我—非我(SNS)模型相比,研究的模型在检测能力和能耗上均有很好的表现。     

机器人学中应用的人工免疫原理、模型与展望

给出人工免疫与机器人学的四个结合模型：负选择、克隆选择、进化免疫、免疫网络．介绍其生物机理,归纳和评述了主要算法及在机器人中的应用成果. 与其它进化算法做了比较,并展望了进一步的研究方向．     

A multi-objective artificial immune algorithm for parameter optimization in support vector machine

Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, σ parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained.     

对一种白盒SM4方案的差分计算分析

传统密码算法的安全性建立在黑盒攻击模型下. 在这种攻击模型下, 攻击者只能获取密码算法的输入输出, 而无法得知密码算法运行时的内部细节. 近年来白盒攻击模型的概念被提出. 在白盒攻击模型下, 攻击者既可以获取密码算法的输入输出, 也可以直接观测或更改密码算法运行时的内部数据. 为保证已有密码算法在白盒攻击环境下的安全性, 在不改变其功能的基础上通过白盒密码技术对其进行重新设计被称为已有密码算法的白盒实现. 研究白盒实现方案的设计与分析对于解决数字版权管理问题具有重要意义. 近年来, 出现了一类针对白盒实现方案的旁信道分析方法. 这类分析手段只需要知道很少白盒实现方案的内部细节, 却可以提取到密钥, 因此是一类对现有白盒实现方案具有实际威胁的分析手段. 对现有白盒实现方案进行此类分析对于确保方案安全性具有重要现实意义. 此类分析方法中的典型代表是基于差分功耗分析原理的差分计算分析. 基于差分计算分析, 对白-武白盒SM4方案进行了安全性分析. 基于对GF(2)上n阶均匀随机可逆矩阵统计特征的研究结果, 提出了一种改进型差分计算分析(IDCA), 可以在分析成功率几乎不变的前提下显著提升分析效率. 结果表明, 白-武白盒SM4方案在面对差分计算分析时不能保证安全性, 必须对其进行进一步改进使之满足实际应用场景下的安全性需求.     

A resource limited artificial immune system algorithm for supervised classification of multi/hyper‐spectral remote sensing imagery

The resource limited artificial immune system (RLAIS), a new computational intelligence approach, is being increasingly recognized as one of the most competitive methods for data clustering and analysis. Nevertheless, owing to the inherent complexity of the conventional RLAIS algorithm, its application to multi/hyper‐class remote sensing image classification has been considerably limited. This paper explores a novel artificial immune algorithm based on the resource limited principles for supervised multi/hyper‐spectral image classification. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: parallelepiped, minimum distance, maximum likelihood, K‐nearest neighbour and back‐propagation neural network. The results show that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and hence provides an effective new option for processing multi/hyper spectral remote sensing images.