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.     

Symbiotic artificial immune system

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.     

A machine learning evaluation of an artificial immune system

ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.     

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%.     

Clustering data in an uncertain environment using an artificial immune system

Clustering of data in an uncertain environment can result into different partitions of the data at different points in time. Therefore, the initial formed clusters of non-stationary data can adapt over time which means that feature vectors associated with different clusters can follow different migration types to and from other clusters. This paper investigates different data migration types and proposes a technique to generate artificial non-stationary data which follows different migration types. Furthermore, the paper proposes clustering performance measures which are more applicable to measure the clustering quality in a non-stationary environment compared to the clustering performance measures for stationary environments. The proposed clustering performance measures in this paper are then used to compare the clustering results of three network based artificial immune models, since the adaptability and self-organising behaviour of the natural immune system inspired the modelling of network based artificial immune models for clustering of non-stationary data.     

Automatic knot adjustment using an artificial immune system for B-spline curve approximation

Reverse engineering transforms real parts into engineering concepts or models. First, sampled points are mapped from the object’s surface by using tools such as laser scanners or cameras. Then, the sampled points are fitted to a free-form surface or a standard shape by using one of the geometric modeling techniques. The curves on the surface have to be modeled before surface modeling. In order to obtain a good B-spline curve model from large data, the knots are usually respected as variables. A curve is then modeled as a continuous, nonlinear and multivariate optimization problem with many local optima. For this reason it is very difficult to reach a global optimum. In this paper, we convert the original problem into a discrete combinatorial optimization problem like in Yoshimoto et al. [F. Yoshimoto, M. Moriyama, T. Harada, Automatic knot placement by a genetic algorithm for data fitting with a spline, in: Proceedings of the International Conference on Shape Modeling and Applications, IEEE Computer Society Press, 1999, pp. 162-169] and Sarfraz et al. [M. Sarfraz, S.A. Raza, Capturing outline of fonts using genetic algorithm and splines, in: Fifth International Conference on Information Visualisation (IV’01), 2001, pp. 738-743]. Then, we suggest a new method that solves the converted problem by artificial immune systems. We think the candidates of the locations of knots as antibodies. We define the affinity measure benefit from Akaike’s Information Criterion (AIC). The proposed method determines the appropriate location of knots automatically and simultaneously. Furthermore, we do not need any subjective parameter or good population of initial location of knots for a good iterative search. Some examples are also given to demonstrate the efficiency and effectiveness of our method.     

An artificial immune system architecture for computer securityapplications

With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java and tested. The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID     

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37?kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.     

人工免疫系统中的云决策*

分析了在人工免疫系统中识别器识别抗原的过程中,由于抗原的属性具有不确定性并且抗原空间异常庞大,传统的单一识别模式带来的高伪肯定率和伪否定率的问题。引入了云模型的理论来应对此类问题,用多个识别器联合对抗原进行云决策,达到降低识别的伪肯定率和伪否定率的目的。     

基于人工免疫原理的多任务协同算法

将免疫系统所特有的免疫记忆、clone选择和亲和力计算应用于工作流协同机制的算法研究中,尝试着探索出用人工免疫理论来解决动态、不确定性计算环境中多任务协同问题的新思路。仿真实验表明,该算法是正确、有效、可行的,而且在运行时间和解的性能上都优于相关算法。     

WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.     

资源有限人工免疫系统的改进

分析了资源有限人工免疫系统的不足,提出了改进的资源有限人工免疫系统。改进的系统在演化过程中每一步都重新计算适应度阈值,更实际地反映了当时的抗体亲和度的状态;选取的刺激度函数充分体现了抗体距离较小时刺激值所应具有的优势,对较小的距离又不过分敏感;同时选择可以使系统更合理地分配抗体的资源分配函数。仿真实验结果说明,改进的人工免疫系统在网络的进化速度、结构等方面获得了较优良的性能。     

基于人工免疫系统的多目标函数优化

为克服传统遗传算法退化和早熟等缺点,同时降低优化算法的复杂度,提出基于人工免疫系统（Artificial Immune System, AIS）实现无约束多目标函数的优化。使用随机权重法和自适应权重法计算种群个体的适应值,使Pareto最优解均匀分布的同时,加快算法的收敛;通过引入人工免疫系统的三个基本算子：克隆、超变异和消亡,保持种群的多样性;在进化种群外设立Pareto 解集,保存历代的近似最优解。使用了两个典型的多目标检测函数验证了该算法的有效性。优化结果表明,基于AIS的多目标优化算法可使进化种群迅速收敛到Pareto前沿,并能均匀分布,是实现多目标函数优化的有效方法。     

Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification

The problem of classifying traffic flows in networks has become more and more important in recent times, and much research has been dedicated to it. In recent years, there has been a lot of interest in classifying traffic flows by application, based on the statistical features of each flow. Information about the applications that are being used on a network is very useful in network design, accounting, management, and security. In our previous work we proposed a classification algorithm for Internet traffic flow classification based on Artificial Immune Systems (AIS). We also applied the algorithm on an available data set, and found that the algorithm performed as well as other algorithms, and was insensitive to input parameters, which makes it valuable for embedded systems. It is also very simple to implement, and generalizes well from small training data sets. In this research, we expanded on the previous research by introducing several optimizations in the training and classification phases of the algorithm. We improved the design of the original algorithm in order to make it more predictable. We also give the asymptotic complexity of the optimized algorithm as well as draw a bound on the generalization error of the algorithm. Lastly, we also experimented with several different distance formulas to improve the classification performance. In this paper we have shown how the changes and optimizations applied to the original algorithm do not functionally change the original algorithm, while making its execution 50–60% faster. We also show that the classification accuracy of the Euclidian distance is superseded by the Manhattan distance for this application, giving 1–2% higher accuracy, making the accuracy of the algorithm comparable to that of a Naïve Bayes classifier in previous research that uses the same data set.     

Re-visiting the artificial immune recognition system: a survey and an improved version

This paper surveys the major works related to an artificial immune system based classifier that was proposed in the 2000s, namely, the artificial immune recognition system (AIRS) algorithm. This survey has revealed that most works on AIRS was dedicated to the application of the algorithm to real-world problems rather than to theoretical developments of the algorithm. Based on this finding, we propose an improved version of the AIRS algorithm which we dub AIRS3. AIRS3 takes into account an important parameter that was ignored by the original algorithm, namely, the number of training antigens represented by each memory cell at the end of learning (numRepAg). Experiments of the new AIRS3 algorithm on data sets taken from the UCI machine learning repository have shown that taking into account the numRepAg information enhances the classification accuracy of AIRS.     

Prototype reduction using an artificial immune model

Artificial immune system (AIS)-based pattern classification approach is relatively new in the field of pattern recognition. The study explores the potentiality of this paradigm in the context of prototype selection task that is primarily effective in improving the classification performance of nearest-neighbor (NN) classifier and also partially in reducing its storage and computing time requirement. The clonal selection model of immunology has been incorporated to condense the original prototype set, and performance is verified by employing the proposed technique in a practical optical character recognition (OCR) system as well as for training and testing of a set of benchmark databases available in the public domain. The effect of control parameters is analyzed and the efficiency of the method is compared with another existing techniques often used for prototype selection. In the case of the OCR system, empirical study shows that the proposed approach exhibits very good generalization ability in generating a smaller prototype library from a larger one and at the same time giving a substantial improvement in the classification accuracy of the underlying NN classifier. The improvement in performance has been statistically verified. Consideration of both OCR data and public domain datasets demonstrate that the proposed method gives results better than or at least comparable to that of some existing techniques.     

基于人工免疫的新型检测器生成模型

继承了人工免疫系统的思想,研究了KIM和BENTLEY的克隆选择算法,提出了一种适用于入侵检测的新的检测器生成模型。其核心在于两个新的算法：一是为了提高检测器的多样性及适应度水平,提出了基于相似性和适应度相结合的概率选择算法,并给出了此类概率选择的一般形式,理论分析了算法中的权重参数α。二是在产生子代检测器时,为了使得父代的优良基因能最大程度地遗传给子代,防止交叉变异中的退化现象,提出了检测器有效因子的概念和使用有效因子进行保优的策略。通过仿真实验证明合适选择α参数以及有效因子的长度阈值Neg,能使该模型具有很好的多样性和自适应性,呈现出较高的“非我”检测率和低的误检率。     

一种基于人工免疫系统的聚类算法

根据数据分析中聚类判断所遵循的原则,模拟抗体捕获抗原的机制,提出了一种基于人工免疫系统的聚类算法,最终可以获得全局最优解,并且具有本质上的并行性、计算效率高和聚类能力强等优点。     

Diagnosis of chest diseases using artificial immune system

Chest diseases are one of the greatest health problems for people living in the developing world. Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are most important chest diseases and these are very common illnesses in Turkey. In this paper, a study on chest diseases diagnosis was realized by using artificial immune system. We obtained the classification accuracy with artificial immune system 93.84%. The result of the study was compared with the results of the previous similar studies reported focusing on chest diseases diagnosis. The chest diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports.     

Robust tile-based texture synthesis using artificial immune system

One significant problem in tile-based texture synthesis is the presence of conspicuous seams in the tiles. The reason is that sample patches employed as primary patterns of the tile set may not be well stitched if carelessly picked. In this paper, we introduce a robust approach that can stably generate an ω-tile set of high quality and pattern diversity. First, an extendable rule is introduced to increase the number of sample patches to vary the patterns in an ω-tile set. Second, in contrast to other concurrent techniques that randomly choose sample patches for tile construction, ours uses artificial immune system (AIS) to select the feasible patches from the input example. This operation ensures the quality of the whole tile set. Experimental results verify the high quality and efficiency of the proposed algorithm.