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.

Artificial immune systems as a novel soft computing paradigm

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 advent of the smart grid promises to usher in an era that will bring intelligence, efficiency, and optimality to the power grid. Most of these changes will occur as an Internet-like communications network is superimposed on top of the current power grid using wireless mesh network technologies with the 802.15.4, 802.11, and WiMAX standards. Each of these will expose the power grid to cybersecurity threats. In order to address this issue, this work proposes a distributed intrusion detection system for smart grids (SGDIDS) by developing and deploying an intelligent module, the analyzing module (AM), in multiple layers of the smart grid. Multiple AMs will be embedded at each level of the smart grid-the home area networks (HANs), neighborhood area networks (NANs), and wide area networks (WANs)-where they will use the support vector machine (SVM) and artificial immune system (AIS) to detect and classify malicious data and possible cyberattacks. AMs at each level are trained using data that is relevant to their level and will also be able to communicate in order to improve detection. Simulation results demonstrate that this is a promising methodology for supporting the optimal communication routing and improving system security through the identification of malicious network traffic.

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Distributed Intrusion Detection System in a Multi-Layer Network Architecture of Smart Grids

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

This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.

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Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

The clonal selection algorithm is used by the natural immune system to define the basic features of an immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. In this paper, we propose a powerful computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The algorithm is shown to be an evolutionary strategy capable of solving complex machinelearning tasks, like pattern recognition and multimodal optimization.

The Clonal Selection Algorithm with Engineering Applications 1

This work applies two immune-inspired algorithms, namely opt-aiNet and omni-aiNet, to train multi-layer perceptrons (MLPs) to be used in the construction of ensembles of classifiers. The main goal is to investigate the influence of the diversity of the set of solutions generated by each of these algorithms, and if these solutions lead to improvements in performance when combined in ensembles. omni-aiNet is a multi-objective optimization algorithm and, thus, explicitly maximizes the components' diversity at the same time it minimizes their output errors. The opt-aiNet algorithm, by contrast, was originally designed to solve single-objective optimization problems, focusing on the minimization of the output error of the classifiers. However, an implicit diversity maintenance mechanism stimulates the generation of MLPs with different weights, which may result in diverse classifiers. The performances of opt-aiNet and omni-aiNet are compared with each other and with that of a second-order gradient-based algorithm, named MSCG. The results obtained show how the different diversity maintenance mechanisms presented by each algorithm influence the gain in performance obtained with the use of ensembles.

Neural network ensembles: immune-inspired approaches to the diversity of components

Search and Optimization.- A Gradient-Based Artificial Immune System Applied to Optimal Power Flow Problems.- Multimodal Dynamic Optimization: From Evolutionary Algorithms to Artificial Immune Systems.- NAIS: A Calibrated Immune Inspired Algorithm to Solve Binary Constraint Satisfaction Problems.- A Solution Concept for Artificial Immune Networks: A Coevolutionary Perspective.- Classification and Clustering.- Artificial Immune Systems for Classification of Petroleum Well Drilling Operations.- The SUPRAIC Algorithm: A Suppression Immune Based Mechanism to Find a Representative Training Set in Data Classification Tasks.- The Influence of Diversity in an Immune-Based Algorithm to Train MLP Networks.- Applying Biclustering to Text Mining: An Immune-Inspired Approach.- Anomaly Detection and Negative Selection.- Defence Against 802.11 DoS Attacks Using Artificial Immune System.- A Novel Immune Inspired Approach to Fault Detection.- Towards a Novel Immune Inspired Approach to Temporal Anomaly Detection.- Bankruptcy Prediction Using Artificial Immune Systems.- Phase Transition and the Computational Complexity of Generating r-Contiguous Detectors.- Real-Valued Negative Selection Algorithm with a Quasi-Monte Carlo Genetic Detector Generation.- A Novel Fast Negative Selection Algorithm Enhanced by State Graphs.- Robotics, Control and Electronics.- Clonal Selection Algorithms for 6-DOF PID Control of Autonomous Underwater Vehicles.- An Immuno Robotic System for Humanitarian Search and Rescue (Application Stream).- The Application of a Dendritic Cell Algorithm to a Robotic Classifier.- On Immune Inspired Homeostasis for Electronic Systems.- Modeling Papers.- Modeling Migration, Compartmentalization and Exit of Naive T Cells in Lymph Nodes Without Chemotaxis.- Revisiting the Central and Peripheral Immune System.- Topological Constraints in the Evolution of Idiotypic Networks.- A Computational Model for the Cognitive Immune System Theory Based on Learning Classifier Systems.- Conceptual Papers.- Motif Detection Inspired by Immune Memory.- An Immune-Inspired Approach to Speckled Computing.- Biological Inspiration for Artificial Immune Systems.- Regulatory T Cells: Inspiration for Artificial Immune Systems.- Technical Papers and General Applications.- Automated Blog Design System with a Population-Based Artificial Immune Algorithm.- Immune and Evolutionary Approaches to Software Mutation Testing.- An Artificial Immune System Based Approach for English Grammar Checking.- A Novel Clonal Selection Algorithm Based Fragile Watermarking Method.- BeeAIS: Artificial Immune System Security for Nature Inspired, MANET Routing Protocol, BeeAdHoc.- A Cultural Immune System for Economic Load Dispatch with Non-smooth Cost Functions.- Artificial Immune System to Find a Set of k-Spanning Trees with Low Costs and Distinct Topologies.- How to Obtain Appropriate Executive Decisions Using Artificial Immunologic Systems.- An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions.

Fernando J. Von Zuben

Papers

The Clonal Selection Algorithm with Engineering Applications 1

Neural network ensembles: immune-inspired approaches to the diversity of components

Artificial immune systems : 6th International Conference, ICARIS 2007 Santos, Brazil, August 26-29, 2007 : proceedings