Showing papers on "Artificial immune system published in 2014"

A novel model for credit card fraud detection using Artificial Immune Systems

Abstract: We added Negative Selection, update and dataset analysis to base AIRS algorithm.Antigen affinity is calculated using a novel method.The model uses a cloud computing solution in order to perform the training phase parallel.We used scoring for flagged records which shows how risky a fraud-flagged record might be. The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this paper we address credit card fraud detection using Artificial Immune Systems (AIS), and introduce a new model called AIS-based Fraud Detection Model (AFDM). We will use an immune system inspired algorithm (AIRS) and improve it for fraud detection. We increase the accuracy up to 25%, reduce the cost up to 85%, and decrease system response time up to 40% compared to the base algorithm.

Constrained Optimization Via Artificial Immune System

Abstract: An artificial immune system inspired by the fundamental principle of the vertebrate immune system, for solving constrained optimization problems, is proposed The analogy between the mechanism of biological immune response and constrained optimization formulation is drawn Individuals in population are classified into feasible and infeasible groups according to their constraint violations that closely match with the two states, inactivated and activated, of B-cells in the immune response Feasible group focuses on exploitation in the feasible areas through clonal selection, recombination, and hypermutation, while infeasible group facilitates exploration along the feasibility boundary via location update Direction information is extracted to promote the interactions between these two groups This approach is validated by the benchmark functions proposed most recently and compared with those of the state of the art from various branches of evolutionary computation paradigms The performance achieved is considered fairly competitive and promising

A survey on software fault detection based on different prediction approaches

Abstract: One of the software engineering interests is quality assurance activities such as testing, verification and validation, fault tolerance and fault prediction. When any company does not have sufficient budget and time for testing the entire application, a project manager can use some fault prediction algorithms to identify the parts of the system that are more defect prone. There are so many prediction approaches in the field of software engineering such as test effort, security and cost prediction. Since most of them do not have a stable model, software fault prediction has been studied in this paper based on different machine learning techniques such as decision trees, decision tables, random forest, neural network, Naive Bayes and distinctive classifiers of artificial immune systems (AISs) such as artificial immune recognition system, CLONALG and Immunos. We use four public NASA datasets to perform our experiment. These datasets are different in size and number of defective data. Distinct parameters such as method-level metrics and two feature selection approaches which are principal component analysis and correlation based feature selection are used to evaluate the finest performance among the others. According to this study, random forest provides the best prediction performance for large data sets and Naive Bayes is a trustable algorithm for small data sets even when one of the feature selection techniques is applied. Immunos99 performs well among AIS classifiers when feature selection technique is applied, and AIRSParallel performs better without any feature selection techniques. The performance evaluation has been done based on three different metrics such as area under receiver operating characteristic curve, probability of detection and probability of false alarm. These three evaluation metrics could give the reliable prediction criteria together.

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

Abstract: The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.

A Survey of Artificial Immune System Based Intrusion Detection

Abstract: In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.

The Adaptive Immune System

Abstract: The human adaptive immune system is a marvel of nature – a complex and dynamic web of cells, tissues, and molecules that serves as a very capable system of defense throughout life. In contrast to the innate immune system that is similar in other animals, the highly regulated mammalian adaptive immune system appears designed to efficiently cope with constantly evolving microbial antigens with relative minimization of collateral damage to the host. After discussion of its major defining characteristics, we explore the building blocks of the adaptive immune system through the basic functional responses to infection.

An AIS-based hybrid algorithm for static job shop scheduling problem

Abstract: A static job shop scheduling problem (JSSP) is a class of JSSP which is a combinatorial optimization problem with the assumption of no disruptions and previously known knowledge about the jobs and machines. A new hybrid algorithm based on artificial immune systems (AIS) and particle swarm optimization (PSO) theory is proposed for this problem with the objective of makespan minimization. AIS is a metaheuristics inspired by the human immune system. Its two theories, namely, clonal selection and immune network theory, are integrated with PSO in this research. The clonal selection theory builds up the framework of the algorithm which consists of selection, cloning, hypermutation, memory cells extraction and receptor editing processes. Immune network theory increases the diversity of antibody set which represents the solution repertoire. To improve the antibody hypermutation process to accelerate the search procedure, a modified version of PSO is inserted. This proposed algorithm is tested on 25 benchmark problems of different sizes. The results demonstrate the effectiveness of the PSO algorithm and the specific memory cells extraction process which is one of the key features of AIS theory. By comparing with other popular approaches reported in existing literatures, this algorithm shows great competitiveness and potential, especially for small size problems in terms of computation time.

Reevaluating Immune-Inspired Hypermutations Using the Fixed Budget Perspective

Abstract: Different studies have theoretically analyzed the performance of artificial immune systems in the context of optimization. It has been noted that, in comparison with evolutionary algorithms and local search, hypermutations tend to be inferior on typical example functions. These studies have used the expected optimization time as performance criterion and cannot explain why artificial immune systems are popular in spite of these proven drawbacks. Recently, a different perspective for theoretical analysis has been introduced, concentrating on the expected performance within a fixed time frame instead of the expected time needed for optimization. Using this perspective we reevaluate the performance of somatic contiguous hypermutations and inverse fitness-proportional hypermutations in comparison with random local search on one well-known example function in which a random local search is known to be efficient and much more efficient than these hypermutations with respect to the expected optimization time. We prove that, depending on the choice of the initial search point, hypermutations can by far outperform random local search in a given time frame. This insight helps to explain the success of seemingly inefficient mutation operators in practice. Moreover, we demonstrate how one can benefit from these theoretically obtained insights by designing more efficient hybrid search heuristics.

Feedback controller design for a boost converter through evolutionary algorithms

Abstract: This study explains a systematic design procedure for the output voltage regulation of a boost-type DC-DC converter employing evolutionary algorithms. The feedback controller design for output voltage regulation is formulated as an optimisation problem and the controller constants are identified via evolutionary search. The design procedure employing genetic algorithm, differential evolution and artificial immune system is lucidly described. Computer simulation results supported by experimental evidence clearly demonstrate that the controllers estimated through evolutionary algorithms are capable of delivering enhanced output voltage regulation under different types of load and supply disturbances.

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Abstract: Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

RFID reader anti-collision algorithm using adaptive hierarchical artificial immune system

Abstract: In a radio-frequency identification (RFID) system, if a group of readers transmit and/or receive signals at the same time, they will probably interfere with each other, so that the resulting reader collision problems (e.g., reader-to-reader collision, reader-to-tag collision) will happen. Generally, the reader-to-reader collision can be mitigated by maximizing the tag identification capability, which is related to frequencies and time slots, so it can be transferred as a resource scheduling problem by optimizing the tag identification capability. Artificial immune system is an emerging heuristic evolutionary method which is widely applied to scientific researches and engineering problems. This paper formulates a reader-to-reader anti-collision model from the viewpoint of resource scheduling and proposes an adaptive hierarchical artificial immune system (RA-AHAIS) to solve this optimization problem. A series of simulation experiments are arranged to analyzing the effects of time slots and frequency. Further simulation experiments are made to compare such performance indices as number of identified tags between the proposed RA-AHIAS and the other existing algorithms. The numerical simulation results indicate that this proposed RA-AHAIS is an effective reader-to-reader anti-collision method, and performs better in tag identification capability and computational efficiency than the other methods, such as genetic algorithm (RA-GA), particle swarm optimization (RA-PSO) and artificial immune system for resource allocation (RA-AIS).

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Abstract: Evolutionary Algorithms (EAs) are a fascinating branch of computational intelligence with much potential for use in many application areas The fundamental principle of EAs is to use ideas inspired by the biological mechanisms observed in nature, such as selection and genetic changes, to find the best solution for a given optimization problem Generally, EAs use iterative processes, by growing a population of solutions selected in a guided random search and using parallel processing, in order to achieve a desired result Such population based approaches, for example particle swarm and ant colony optimization (inspired from biology), are among the most popular metaheuristic methods being used in machine learning, along with others such as the simulated annealing (inspired from thermodynamics) In this paper, we provide a short survey on the state-of-the-art of EAs, beginning with some background on the theory of evolution and contrasting the original ideas of Darwin and Lamarck; we then continue with a discussion on the analogy between biological and computational sciences, and briefly describe some fundamentals of EAs, including the Genetic Algorithms, Genetic Programming, Evolution Strategies, Swarm Intelligence Algorithms (ie, Particle Swarm Optimization, Ant Colony Optimization, Bacteria Foraging Algorithms, Bees Algorithm, Invasive Weed Optimization), Memetic Search, Differential Evolution Search, Artificial Immune Systems, Gravitational Search Algorithm, Intelligent Water Drops Algorithm We conclude with a short description of the usefulness of EAs for Knowledge Discovery and Data Mining tasks and present some open problems and challenges to further stimulate research

Artificial Immune System Based Intrusion Detection: Innate Immunity using an Unsupervised Learning Approach

Abstract: This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and real-time training for the adaptive immune system within the artificial immune system. Different methods for unsupervised machine learning are investigated and DBSCAN (density-based spatial clustering of applications with noise) is selected to be utilized in this architecture. The adaptive immune system in our proposed architecture also takes advantage of the distributed structure, which has shown better self-improvement rate compare to centralized mode and provides primary and secondary immune response for unknown anomalies and zero-day attacks. The experimental results of proposed architecture is presented and discussed.

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

Abstract: 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.

An improved immune inspired hyper-heuristic for combinatorial optimisation problems

Abstract: The meta-dynamics of an immune-inspired optimisation system NELLI are considered. NELLI has previously shown to exhibit good performance when applied to a large set of optimisation problems by sustaining a network of novel heuristics. We address the mechanisms by which new heuristics are defined and subsequently generated. A new representation is defined, and a mutation-based operator inspired by clonal-selection introduced to control the balance between exploration and exploitation in the generation of new network elements. Experiments show significantly improved performance over the existing system in the bin-packing domain. New experiments in the job-scheduling domain further show the generality of the approach.

A cognitive approach for botnet detection using Artificial Immune System in the cloud

Abstract: The advent of cloud computing has given a provision for both good and malicious opportunities. Virtualization itself as a component of Cloud computing, has provided users with an immediate way of accessing limitless resource infrastructures. Botnets have evolved to be the most dangerous group of remote- operated zombie computers given the open cloud environment. They happen to be the dark side of computing due to the ability to run illegal activities through remote installations, attacks and propagations through exploiting vulnerabilities. The problem that this paper addresses is that botnet technology is advancing each day and detection in the cloud is becoming hard. In this paper, therefore, the authors' presents an approach for detecting an infection of a robot network in the cloud environment. The authors proposed a detection mechanism using Artificial Immune System (AIS). The results show that this research is significant.

Text Clustering using Distances Combination by Social Bees: Towards 3D Visualisation Aspect

Abstract: Recently, the researchers proved that 90% of the information existed on the web, were presented in unstructured format text free. The automatic text classification clustering, has become a crucial challenge in the computer science community, where Most of the classical techniques, have known different problems in terms of time execution, multiplicity of data marketing, biology, economics, and the initialization of cluster number. Nowadays, the bio-inspired paradigm, has known a genuine success in several sectors and particularly in the world of data-mining. The content of our work, is a novel approach called distances combination by social bees DC-SB for text clustering, composed of four steps: Pre-processing using different methods of texts representation bag of words and n-gram characters and the weighting TF-IDF, for the construction of the vectors; Bees' artificial life, the authors have imitated the functioning of social bees using three artificial worker beescleaner, guardian and forager where each one of them is characterized by a distance measure different to others generated from the artificial queen centroid of the cluster hive; Clustering using the concept of filtering where each filter is controlled by an artificial worker, and a document must pass three different obstacles to be added to the cluster. For the experiments they use the benchmark Reuters 21578 and a variety of validation tools execution time f-measure and entropy with a variation of parameters threshold, distance measures combination and texts representation. The authors have compared their results with the performances of other methods existed in literature Cellular Automata 2D, Artificial Immune System AIS and Artificial Social Spiders ASS, the conclusion obtained prove that the approach can solve the text clustering problem; finally, the visualization step, which provides a 3D navigation of the results obtained by the mean of a global and detailed view of the hive and the apiary, using the functionality of zooming and rotation.

Negative selection algorithm based on grid file of the feature space

Abstract: Negative selection algorithm (NSA) is an important algorithm for the generation of artificial immune detectors. However, the randomly generated candidate detectors have to be compared with the whole self set to exclude self reactive detectors. The inefficiency of the comparing process seriously limited the application of immune algorithms. Therefore, a new negative selection algorithm GF-RNSA is proposed in the paper. Firstly, the feature space is divided into a number of grid cells, and then detectors are separately generated in each cell. As candidate detectors just need to compare with the self antigens located in the same cell rather than with the whole self set, the detector training can be more efficient. The theoretical analysis demonstrated that the time complexity of GF-RNSA is effectively reduced that the exponential relationships between self size and time complexity in traditional NSAs is eliminated. The experimental results showed that: not only the time cost of negative selection, but also the time cost of data preprocess and detection are reduced, while the detection accuracy is not much declined.

Evolutionary algorithms and artificial immune systems on a bi-stable dynamic optimisation problem

Abstract: Dynamic optimisation is an important area of application for evolutionary algorithms and other randomised search heuristics. Theoretical investigations are currently far behind practical successes. Addressing this deficiency a bi-stable dynamic optimisation problem is introduced and the performance of standard evolutionary algorithms and artificial immune systems is assessed. Deviating from the common theoretical perspective that concentrates on the expected time to find a global optimum (again) here the `any time performance' of the algorithms is analysed, i.e., the expected function value at each step. Basis for the analysis is the recently introduced perspective of fixed budget computations. Different dynamic scenarios are considered which are characterised by the length of the stable phases. For each scenario different population sizes are examined. It is shown that the evolutionary algorithms tend to have superior performance in almost all cases.

Simultaneous scheduling of machines and tools in multimachine flexible manufacturing systems using artificial immune system algorithm

Abstract: This article deals with simultaneous scheduling of machines and tools in a multimachine flexible manufacturing system to generate best optimal sequences that minimise makespan. As flexible manufacturing system (FMS) is an integrated manufacturing facility, simultaneous scheduling of different components of FMS is essential. In this research work, attempts have been made to integrate machines and tools. The aim of this article is to address combined machine and tool scheduling in a FMS consisting of machines and a central tool magazine. Four heuristics, priority dispatching rules, modified nondelay schedule generation algorithm with six different priority dispatching rules, modified Giffler and Thompson algorithm and artificial immune system (AIS) algorithm, have been proposed for solving combined machine and tool problems with makespan as objective. The proposed heuristics are numerically tested on various problems and the results are compared. The result shows that AIS algorithm yields better results for...

An Online Fault Diagnosis Strategy for Full Operating Cycles of Chemical Processes

Abstract: Online fault diagnosis is one of the most important methods to ensure stability and safety in many chemical processes. In this work, a lab-scale distillation process is designed and built for fault diagnosis study, and the online fault diagnosis system (OFDS) is developed with a distributed control system (DCS) system and a real-time database. Artificial neural networks (ANNs) are used for startup state judgment and for fault detection in the steady state, while the dynamic artificial immune system (DAIS) is used for fault detection in the startup phase and for fault identification in both the startup phase and the steady state. The results of case studies clearly illustrate that the developed system is efficient in online fault diagnosis of distillation processes during the full operating cycle, especially when the number of historical fault samples is limited. The self-learning ability of the methods ensures that the system can remember and diagnose new faults, and the friendly interface of OFDS can sho...

Smartphone malware detection model based on artificial immune system

Abstract: In order to solve the problem that the traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model (SP-MDM) based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

A fast anomaly detection system using probabilistic artificial immune algorithm capable of learning new attacks

Abstract: In this paper, we propose anomaly based intrusion detection algorithms in computer networks using artificial immune systems, capable of learning new attacks. Unique characteristics and observations specific to computer networks are considered in developing faster algorithms while achieving high performance. Although these characteristics play a key role in the proposed algorithms, we believe they have been neglected in the previous related works. We evaluate the proposed algorithms on a number of well-known intrusion detection datasets, as well as two new real datasets extracted from the data networks for intrusion detection. We analyze the detection performance and learning capabilities of the proposed algorithms, in addition to performance criteria such as false alarm rate, detection rate, and response time. The experimental results demonstrate that the proposed algorithms exhibit fast response time, low false alarm rate, and high detection rate. They can also learn new attack patterns, and identify them the next time they are introduced to the network.

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

Abstract: This paper presents the development of a biologically inspired generalized conceptual framework for the detection, identification, evaluation, and accommodation of aircraft subsystem abnormal conditions The artificial immune system paradigm in conjunction with other artificial intelligence techniques, analytical tools, and heuristics are used in an attempt to provide a comprehensive solution to the problem of safely operating aircraft under abnormal flight conditions The main concepts and foundations are established, and methodologies and algorithms for implementation are outlined The approach addresses directly the complexity and multidimensionality of aircraft dynamic response in the context of abnormal conditions and is expected to facilitate the design of onboard augmentation systems to increase aircraft survivability, improve operation safety, and optimize performance at both normal and abnormal/upset conditions Results obtained with an example implementation are presented to illustrate the poten