https://dialnet.unirioja.es/descarga/articulo/4511387.pdf

The origins of order. Self-organization and selection in evolution

The Quark and the Jaguar

Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

/pdf/review-the-use-of-computational-intelligence-in-intrusion-1r8rwmtm1h.pdf

Review: The use of computational intelligence in intrusion detection systems: A review

During the last decade, the field of artificial immune system (A1S) is progressing slowly and steadily as a branch of computational intelligence (CI). There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts - computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the artificial immune system (AIS) is emerging as an active and attractive, field involving models, techniques and applications of greater diversity

Advances in artificial immune systems

The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications.

Review Article: Recent Advances in Artificial Immune Systems: Models and Applications

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.

/pdf/is-negative-selection-appropriate-for-anomaly-detection-3w9pf62971.pdf

Is negative selection appropriate for anomaly detection

We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles,
and we outline such a framework here, in the context of Artificial Immune System (AIS) network models, and we discuss mathematical techniques for analysing the state dynamics of AIS. We further propose ways to
unify several domains into a common meta-framework, in the context of AIS population models. We finally discuss a case study, and hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological
domain.

/pdf/conceptual-frameworks-for-artificial-immune-systems-2cx58dax7m.pdf

Conceptual Frameworks for Artificial Immune Systems

1. The challengeA gateway event [35] is a change to a system that leads to the possibility of huge increases inkinds and levels of complexity. It opens up a whole new kind of phase space to the system’sdynamics.Gatewayeventsduringevolutionoflifeonearthincludetheappearanceofeukaryotes(organisms with a cell nucleus), an oxygen atmosphere, multi-cellular organisms and grass.Gatewayeventsduringthedevelopmentofmathematicsincludeeachinventionofanewclassofnumbers (negative, irrational, imaginary, ...), and dropping Euclid’s parallel postulate.A gateway event produces a profound and fundamental change to the system: Oncethrough the gateway, life is never the same again. We are currently poised on the threshold ofa signiﬁcant gateway event in computation: That of breaking free from many of our current“classical computational” assumptions. Our Grand Challenge for computer science isto journey through the gateway event obtained by breaking our current classicalcomputational assumptions, and thereby develop a mature science of Non-ClassicalComputation2. Journeys versus goals

/pdf/journeys-in-non-classical-computation-i-a-grand-challenge-4w3axbu5wl.pdf

Journeys in non-classical computation I: A grand challenge for computing research

We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of AIS network models. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.

/pdf/towards-a-conceptual-framework-for-artificial-immune-systems-4ha4gfsp0w.pdf

Towards a Conceptual Framework for Artificial Immune Systems

The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters. From a computational viewpoint, the immune system has much to offer by way of inspiration. Recently there has been growing interest in the use of the natural immune system as inspiration for the creation of novel approaches to computational problems; this field of research is referred as Immunological Computation (IC) or Artificial Immune Systems (AIS). This chapter describes the physiology of the immune system and provides a general introduction to Artificial Immune Systems. Significant applications that are relevant to data mining, in particular in the areas of machine learning and data analysis are discussed in detail. Attention is paid both to the salient characteristics of the application and the details of the algorithms. This chapter concludes with an evaluation of the current and future contributions of Artificial Immune Systems in Data Mining.

Jonathan Timmis

Papers

Is negative selection appropriate for anomaly detection

Conceptual Frameworks for Artificial Immune Systems

Journeys in non-classical computation I: A grand challenge for computing research

Towards a Conceptual Framework for Artificial Immune Systems

Artificial Immune Systems: Using the Immune System as Inspiration for Data Mining