Showing papers on "Artificial immune system published in 2003"

Artificial immune systems as a novel soft computing paradigm

Abstract: Artificial immune systems (AIS) can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. Their development and application domains follow those of soft computing paradigms such as artificial neural networks (ANN), evolutionary algorithms (EA) and fuzzy systems (FS). Despite some isolated efforts, the field of AIS still lacks an adequate framework for design, interpretation and application. This paper proposes one such framework, discusses the suitability of AIS as a novel soft computing paradigm and reviews those works from the literature that integrate AIS with other approaches, focusing ANN, EA and FS. Similarities and differences between AIS and each of the other approaches are outlined. New trends on how to create hybrids of these paradigms and what could be the benefits of this hybridization are also presented.

Danger theory: The link between AIS and IDS?

Abstract: We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Artificial immune system (AIS) research in the last five years

Abstract: Immunity-based techniques are gaining popularity in wide area of applications, and emerging as a new branch of artificial intelligence (AI). The paper surveys the major works in this field during the last five years, in particular, it reviews the works of existing methods and the new initiatives.

Use of an Artificial Immune System for Job Shop Scheduling

Abstract: In this paper, we propose an algorithm based on an artificial immune system to solve job shop scheduling problems. The approach uses clonal selection, hypermutations and a library of antibodies to construct solutions. It also uses a local selection mechanism that tries to eliminate gaps between jobs in order to improve solutions produced by the search mechanism of the algorithm. The proposed approach is compared with respect to GRASP (an enumerative approach) in several test problems taken from the specialized literature. Our results indicate that the proposed algorithm is highly competitive, being able to produce better solutions than GRASP in several cases, at a fraction of its computational cost.

AISEC: an artificial immune system for e-mail classification

Abstract: With the increase in information on the Internet, the strive to find more effective tools for distinguishing between interesting and non-interesting material is increasing. Drawing analogies from the biological immune system, this paper presents an immune-inspired algorithm called AISEC that is capable of continuously classifying electronic mail as interesting and non-interesting without the need for re-training. Comparisons are drawn with a naive Bayesian classifier and it is shown that the proposed system performs as well as the naive Bayesian system and has a great potential for augmentation.

Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective

Abstract: Since their development, AIS have been used for a number of machine learning tasks including that of classification. Within the literature, there appears to be a lack of appreciation for the possible bias in the selection of various representations and affinity measures that may be introduced when employing AIS in classification tasks. Problems are then compounded when inductive bias of algorithms are not taken into account when applying seemingly generic AIS algorithms to specific application domains. This paper is an attempt at highlighting some of these issues. Using the example of classification, this paper explains the potential pitfalls in representation selection and the use of various affinity measures. Additionally, attention is given to the use of negative selection in classification and it is argued that this may be not an appropriate algorithm for such a task. This paper then presents ideas on avoiding unnecessary mistakes in the choice and design of AIS algorithms and ultimately delivered solutions.

TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model

Abstract: Artificial immune system (AIS) models hold many promises in the field of unsupervised learning. However, existing models are not scalable, which makes them of limited use in data mining. We propose a new AIS based clustering approach (TECNO-STREAMS) that addresses the weaknesses of current AIS models. Compared to existing AIS based techniques, our approach exhibits superior learning abilities, while at the same time, requiring low memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to other approaches is expected to be its ease of adaptation to the dynamic environment that characterizes several applications, particularly in mining data streams. We illustrate the ability of the proposed approach in detecting clusters in noisy data sets, and in mining evolving user profiles from Web clickstream data in a single pass. TECNO-STREAMS adheres to all the requirements of clustering data streams: compactness of representation, fast incremental processing of new data points, and clear and fast identification of outliers.

A study of artificial immune systems applied to anomaly detection

Abstract: The main goal of this research is to examine and to improve the anomaly detection function of artificial immune systems, specifically the negative selection algorithm and other self/non-self recognition techniques This research investigates different representation schemes for the negative selection and proposes new detector generation algorithms suitable for such representations Accordingly, different representations are explored: hyper-rectangles (which can be interpreted as rules), fuzzy rules, and hyper-spheres Four different detector generation algorithms are proposed: Negative Selection with Detection Rules (NSDR, an evolutionary algorithm to generate hypercube detectors), Negative Selection with Fuzzy Detection Rules (NSFDR, an evolutionary algorithm to generate fuzzy-rule detectors), Real-valued Negative Selection (RNS, a heuristic algorithm to generate hyper-spherical detectors), and Randomized Real-valued Negative Selection (RRNS, an algorithm for generating hyper-spherical detectors based on Monte Carlo methods) Also, a hybrid immune learning algorithm, which combines RNS (or RRNS) and classification algorithms is developed This algorithm allows the application of a supervised learning technique even when samples from only one class (normal) are available Different experiments are performed with synthetic and real world data from different sources The experimental results show that the proposed representations along with the proposed algorithms provide some advantages over the binary negative selection algorithm The most relevant advantages include improved scalability, more expressiveness that allows the extraction of high-level domain knowledge, non-crisp distinction between normal and abnormal, and better performance in anomaly detection

Meta-stable Memory in an Artificial Immune Network

Abstract: This paper describes an artificial immune system algorithm which implements a fairly close analogue of the memory mechanism proposed by Jerne [1] (usually known as the Immune Network Theory). The algorithm demonstrates the ability of these types of network to produce meta-stable structures representing populated regions of the antigen space. The networks produced retain their structure indefinitely and capture inherent structure within the sets of antigens used to train them. Results from running the algorithm on a variety of data sets are presented and shown to be stable over long time periods and wide ranges of parameters. The potential of the algorithm as a tool for multivariate data analysis is also explored.

Developing an immunity to spam

Abstract: Immune systems protect animals from pathogens, so why not apply a similar model to protect computers? Several researchers have investigated the use of an artificial immune system to protect computers from viruses and others have looked at using such a system to detect unauthorized computer intrusions. This paper describes the use of an artificial immune system for another kind of protection: protection from unsolicited email, or spam.

Development and Prospect of the Artificial Immune System

Abstract: The history,research areas and development directions of the artificial immune system are reviewedThe research on immune mechanism,algorithm and application are emphasized The similarities and differences between the AIS and evolutionary algorithms,neural networks,general optimized algorithm are studiedBased on the disadvantage of AIS,the development directions are discussed

An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory

Abstract: The standard Genetic Algorithm has several limitations when dealing with dynamic environments. The most harmful limitation as to do with the tendency for the large majority of the members of a population to convergence prematurely to a particular region of the search space, making thus difficult for the GA to find other solutions when changes in the environment occur. Several approaches have been tested to overcome this limitation by introducing diversity in the population or through the incorporation of memory in order to help the algorithm when situations of the past can be observed in future situations. In this paper, we propose a GA inspired in the immune system ideas in order to deal with dynamic environments. This algorithm combines the two aspects mentioned above: diversity and memory and we will show that our algorithm is also more adaptable and accurate than the other algorithms proposed in the literature.

An investigation of the negative selection algorithm for fault detection in refrigeration systems

Abstract: Failure of refrigerated cabinets costs millions annually to supermarkets, and a large market exists for systems which can predict such failures. Previous work, now moving towards deployment, has used neural networks to predict volumes of alarms from refrigeration system controllers, and also to predict likely refrigerant gas loss. Here, we use in-cabinet temperature data, aiming to predict faults from the pattern of temperature over time. We argue that artificial immune systems (AIS) are particularly appropriate for this, and report a series of preliminary experiments which investigate parameter and strategy choices. We also investigate a ‘differential’ encoding scheme designed to highlight essential elements of in-cabinet temperature patterns. The results prove feasibility for AIS in this application, with good self-detection rates, and a promising fault-detection rate. The best configuration of those examined seems to be that which uses the novel differential encoding with r-bits matching.

Information Immune Systems

Abstract: The concept of an information immune system (IIS) is introduced, in which undesirable information is eliminated before it can reach the user. The IIS is inspired by the natural immune systems that protect us from pathogens. IISs from multiple individuals can be combined to form a group IIS which filters out information undesirable to any of the members. The relationship between our proposed IIS architecture and the natural immune system is outlined, and potential applications, including information filtering, interactive design, and collaborative design, are discussed.

Multiobjective optimization using ideas from the clonal selection principle

Abstract: In this paper, we propose a new multiobjective optimization approach based on the clonal selection principle. Our approach is compared with respect to other evolutionary multiobjective optimization techniques that are representative of the state-of-the-art in the area. In our study, several test functions and metrics commonly adopted in evolutionary multiobjective optimization are used. Our results indicate that the use of an artificial immune system for multiobjective optimization is a viable alternative.

Robot error detection using an artificial immune system

Abstract: Biology has produced living creatures that exhibit remarkable fault tolerance. The immune system is one feature that enables this. The acquired immune system learns during the life of the individual to differentiate between self (that which is normally present) and non-self (that which is not normally present). This paper describes an artificial immune system (AIS) that is used as an error detection system and is applied to two different robot based applications; the immunization of a fuzzy controller for a Khepera robot that provides object avoidance and a control module of a BAE Systems RASCAL/sup TM/ robot. The AIS learns normal behavior (unsupervised) during a fault free learning period and then identifies all error greater that a preset error sensitivity. The AIS was implemented in software but has the potential to be implemented in hardware. The AIS can be independent to the system under test, just requiring the inputs and outputs. This is not only ideal in terms of common mode and design errors but also offers the potential of a general, off-the-shelf, error detection system; the same AIS was applied to both the applications.

An investigation into the source of power for AIRS, an artificial immune classification system

Abstract: The AIRS classifier, based on metaphors from the field of artificial immune systems, has shown itself to be an effective general purpose classifier across a broad spectrum of classification problems This research examines the new classifier empirically, replacing one of the two likely sources of its classification power with alternative modifications The results are slightly less effective, but not statistically significantly so We conclude that the modifications, which are computationally somewhat more efficient, provide fast test versions of AIRS for users to experiment with We also conclude that the chief source of classification power of AIRS must lie in its replacement and maintenance of its memory cell population

A scalable artificial immune system model for dynamic unsupervised learning

Abstract: Artificial Immune System (AIS) models offer a promising approach to data analysis and pattern recognition. However, in order to achieve a desired learning capability (for example detecting all clusters in a dat set), current models require the storage and manipulation of a large network of B Cells (with a number often exceeding the number of data points in addition to all the pairwise links between these B Cells). Hence, current AIS models are far from being scalable, which makes them of limited use, even for medium size data sets. We propose a new scalable AIS learning approach that exhibits superior learning abilities, while at the same time, requiring modest memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to current approaches is expected to be its ease of adaptation in dynamic environments. We illustrate the ability of the proposed approach in detecting clusters in noisy data.

Increasing the accuracy of a spam-detecting artificial immune system

Abstract: Spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. Even as anti-spam solutions change to limit the amount of spam sent to users, the senders adapt to make sure their messages are seen. This paper looks at application of the artificial immune system model to protect email users effectively from spam. In particular, it tests the spam immune system against the publicly available spam assassin corpus of spam and non-spam, and extends the original system by looking at several methods of classifying email messages with the detectors produced by the immune system. The resulting system classifies the messages with similar accuracy to other spam filters, but uses fewer detectors to do so, making it an attractive solution for circumstances where processing time is at a premium.

MILA: multilevel immune learning algorithm

Abstract: The biological immune system is an intricate network of specialized tissues, organs, cells, and chemical molecules. T-cell-dependent humoral immune response is one of the complex immunological events, involving interaction of B cells with antigens (Ag) and their proliferation, differentiation and subsequent secretion of antibodies (Ab). Inspired by these immunological principles, we proposed a Multilevel Immune Learning Algorithm (MILA) for novel pattern recognition. It incorporates multiple detection schema, clonal expansion and dynamic detector generation mechanisms in a single framework. Different test problems are studied and experimented with MILA for performance evaluation. Preliminary results show that MILA is flexible and efficient in detecting anomalies and novelties in data patterns.

Mining Evolving User Profiles in Noisy Web Clickstream Data with a Scalable Immune System Clustering Algorithm

Abstract: Web usage mining has recently attracted attention as a viable framework for extracting useful access pattern information, such as user profiles, from massive amounts of Web log data for the purpose of Web site personalization and organization. These efforts have relied mainly on clustering or association rule discovery as the enabling data mining technologies. Typically, data mining has to be completely re-applied periodically and offline on newly generated Web server logs in order to keep the discovered knowledge up to date. In addition to difficulty to scale and adapt in the face of large data and continuously evolving patterns, most clustering techniques, such as the majority of KMeans variants, also suffer from one or more of the following limitations: requirement of the specification of the correct number of clusters/profiles in advance, sensitivity to initialization, sensitivity to the presence of noise and outliers in the data, and unsuitability for sparse data sets. Hence, there is a crucial need for scalable, noise insensitive, initialization independent techniques that can continuously discover possibly evolving Web user profiles without any stoppages or reconfigurations. In this paper, we propose a new scalable clustering methodology that gleams inspiration from the natural immune system to be able to continuously learn and adapt to new incoming patterns. The Web server plays the role of the human body, and the incoming requests play the role of foreign antigens/bacteria/viruses that need to be detected by the proposed immune based clustering technique. Hence, our clustering algorithm plays the role of the cognitive agent of an artificial immune system, whose goal is to continuously perform an intelligent organization of the incoming noisy data into clusters. Our approach exhibits superior learning abilities, while at the same time, requiring modest memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to current approaches is expected to be its ease of adaptation to the dynamic environment that characterizes several applications, particularly in mining data streams. We illustrate the ability of the proposed approach in mining user profiles from Web clickstream data in a single pass under different usage trend sequencing scenarios.

A Danger Theory Inspired Approach to Web Mining

Abstract: Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and explores the relevance of this theory to the application domain of web mining. Central to the idea of Danger theory is that of a context dependant response to invading pathogens. This paper argues that this context dependency could be utilised as powerful metaphor for applications in web mining. An illustrative example adaptive mailbox filter is presented that exploits properties of the immune system, including the Danger theory. This is essentially a dynamical classification task: a task that this paper argues is well suited to the field of artificial immune systems, particularly when drawing inspiration from the Danger theory.

Artificial immune system programming for symbolic regression

Abstract: Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism. This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming. Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in program induction.

Design of an artificial immune system as a novel anomaly detector for combating financial fraud in the retail sector

Abstract: The retail sector often does not possess sufficient knowledge about potential or actual frauds. This requires the retail sector to employ an anomaly detection approach to fraud detection. To detect anomalies in retail transactions, the fraud detection system introduced in this work implements various salient features of the human immune system. This novel artificial immune system, called CIFD (computer immune system for fraud detection), adopts both negative selection and positive selection to generate artificial immune cells. CIFD also employs an analogy of the self-major histocompatability complex (MHC) molecules when antigen data is presented to the system. These novel mechanisms are expected to improve the scalability of CIFD, which is designed to process gigabytes or more of transaction data per day. In addition, CIFD incorporates other prominent features of the HIS such as clonal selection and memory cells, which allow CIFD to behave adaptively as transaction patterns change.

Artificial Immune System Approaches for Aerospace Applications

Abstract: Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

An immune-based approach to document classification

Abstract: The human immune system is a complex adaptive system which has provided inspiration for a range of innovative problem solving techniques in areas such as computer security, knowledge management and information retrieval. In this paper the construction and performance of a novel immune-based learning algorithm is explored whose distributed, dynamic and adaptive nature offers many potential advantages over more traditional models. Through a process of cooperative coevolution a classifier is generated which consists of a set of detectors whose local dynamics enable the system as a whole to group positive and negative examples of a concept. The immune-based learning algorithm is first validated on a standard dataset. Then, combined with an HTML feature extractor, it is tested on a web-based document classification task and found to outperform traditional classification paradigms. Further applications in content filtering, recommendation systems and user profile generation are also directly relevant to the work presented.

An immune learning classifier network for autonomous navigation

Abstract: This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.

A Multi-Layered Immune Inspired Approach to Data Mining

Abstract: A Multi-Layered Immune Inspired Approach to Data Mining. Thomas Knight and Jon Timmis Computing Laboratory University of Kent at Canterbury. CT2 7NF. UK e-mail: tpk1, jt6@ukc.ac.uk Keywords: Machine Learning, Artificial Immune Systems, Data Mining. Abstract Soft computing has been described as computational systems that exploit tolerance for imprecision, uncertainty, partial truth and approximation [1]. Examples of which include artificial neural networks, fuzzy systems, evolutionary algorithms and probabilistic reasoning. Artificial immune systems have recently been proposed as an additional soft computing paradigm [2]. In that paper, the authors argue that artificial immune systems exhibit similar characteristics to other soft computing paradigms which can be used to complement and augment existing soft computing techniques. Artificial Immune Systems are adaptive systems, inspired by theoretical immunology and observed immune functions, principles, and models, which are applied to problem solving [3]. An immune inspired data mining algorithm was proposed in [4], called RAIN (Resource-limited Artificial Immune Network). The motivation behind this algorithm was to abstract processes and characteristics from the immune system, more specifically the immune network theory [5] to develop a novel unsupervised machine learning algorithm. RAIN's artificial immune networks were applied to unsupervised machine learning benchmark data and was reported to perform well. It was anticipated that this algorithm would be suitable for continuous learning with initial work showing that stable populations within the networks could be achieved. However, more recent work has since shown that these networks suffer strong evolutionary pressure and converge to the strongest class represented in the data [6]. Whilst this is not perceived as a major problem from a data mining perspective, for use in continuous learning, however, it would be more desirable if the networks converged to a stable population that is representative of all of the classes in the data and these patterns then remained in the absence of antigenic stimulation (training data). These observations prompted a step back from the existing work to re-evaluate the approaches taken. It was noted that a more holistic approach may provide a better solution in the search for an immune inspired data mining algorithm capable of continuous learning. Rather than focusing on the immune network theory, we have adopted aspects of the primary and secondary responses seen in the adaptive immune system. A multi-layered approach has been devised that incorporates interactions between free-antibodies, b-cells, and memory cells using clonal-selection processes as the core element of the algorithm. This three-layered approach consists of: a free-antibody layer, a b-cell layer and a memory layer. The free-antibody layer provides a general search and pattern recognition function. The b-cell layer provides a more refined pattern recognition function, with the memory layer providing a stable memory structure that is no longer influenced by strong evolutionary pressure. Central to the algorithm is feedback that occurs between the b-cell layer and the free-antibody layer: this produces co-stimulation between b-cells and is part of the secondary immune response in the algorithm. Novel data are incorporated into the b-cell layer and is given a chance to thrive, thus providing a primary immune response. Although the algorithm has been designed for continuous learning, initial testing has highlighted good performance at static clustering and is thus reported in this paper. Initial studies of the algorithm suggests that the performance compares favorably to the nearest immune inspired competitor, a combined clonal selection and immune network based algorithm called aiNet [7]. Although the algorithm itself is very simple, it achieves good representation of data (Figure 1), and compression ratios. (a) (b) Figure 1 (a) shows the 3-circle training set (600 items), and (b) shows the patterns in the memory layer evolved by the new algorithm after 5 iterations (58 items) Preliminary results are encouraging and it is thought the proposed algorithm can be adapted for continuous learning. It is also proposed that this algorithm augments the framework for AIS proposed in [2] with the addition of a new immune inspired algorithm. Citations [1] Y. Jin, ''What is Soft Computing,'' in A Definition Of Soft Computing - adapted from L.A. Zadeh, vol. 2002, 2002. [2] L. N. de Castro and J. I. Timmis, ''Artificial Immune Systems as a Novel Soft Computing Paradigm,'' Soft Computing, vol. In Press, 2002. [3] J. Timmis and L. N. De Castro, ''A Framework for Engineering Artificial Immune Systems,'' in Artificial Immune Systems: A New Computational Intelligence Approach: Springer Verlag, 2002, pp. 53-108. [4] J. Timmis and M. Neal, ''A resource limited artificial immune system for data analysis,'' Knowledge Based Systems, vol. 14, pp. 121-130, 2001. [5] N. K. Jerne, ''Towards a Network theory of the Immune System,'' Annals of Immunology, vol. 125C, pp. 373-389, 1974. [6] T. P. Knight and J. I. Timmis, ''AINE: An immunological approach to data mining,'' presented at IEEE International Conference on Data Mining, San Jose, CA. USA, 2001. [7] L. N. de Castro and F. N. Von Zuben, ''An Evolutionary Immune Network for Data Clustering,'' Proceedings of the IEEE Computer Society Press, SBRN'00, vol. 1, pp. 84-89, 2000. Acknowledgements Thomas Knight would like to thank the SUN(tm) Microsystems for their continued financial support for his PhD Studies.

An Artificial Immune System Approach to Semantic Document Classification

Abstract: AIRS, a resource limited artificial immune classifier system, has performed well on elementary classification tasks. This paper proposes the use of this system for the more complex task of hierarchical, multi-class document classification. This information can then be applied to the realm of taxonomy mapping, an active research area with far reaching implications. Our motivation comes from the use of a personal semantic structure for ease of navigation within a set of Internet based documents.