Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Taxonomy learning from Malay texts using artificial immune system based clustering

In taxonomy learning from texts, the extracted features that are used to describe the context of a term usually are erroneous and sparse. Various attempts to overcome data sparseness and noise have been made using clustering algorithm such as Hierarchical Agglomerative Clustering (HAC), Bisecting K-means and Guided Agglomerative Hierarchical Clustering (GAHC). However these methods suffer low recall. Therefore, the purpose of this study is to investigate the application of two hybridized artificial immune system (AIS) in taxonomy learning from Malay text and develop a Google-based Text Miner (GTM) for feature selection to reduce data sparseness. Two novel taxonomy learning algorithms have been proposed and compared with the benchmark methods (i.e., HAC, GAHC and Bisecting K-means). The first algorithm is designed through the hybridization of GAHC and Artificial Immune Network (aiNet) called GCAINT (Guided Clustering and aiNet for Taxonomy Learning). The GCAINT algorithm exploits a Hypernym Oracle (HO) to guide the hierarchical clustering process and produce better results than the benchmark methods. However, the Malay HO introduces erroneous hypernym-hyponym pairs and affects the result. Therefore, the second novel algorithm called CLOSAT (Clonal Selection Algorithm for Taxonomy Learning) is proposed by hybridizing Clonal Selection Algorithm (CLONALG) and Bisecting k-means. CLOSAT produces the best results compared to the benchmark methods and GCAINT. In order to reduce sparseness in the obtained dataset, the GTM is proposed. However, the experimental results reveal that GTM introduces too many noises into the dataset which leads to many false positives of hypernym-hyponym pairs. The effect of different combinations of affinity measurement (i.e., Hamming, Jaccard and Rand) on the performance of the developed methods was also studied. Jaccard is found better than Hamming and Rand in measuring the similarity distance between terms. In addition, the use of Particle Swarm Optimization (PSO) for automatic parameter tuning the GCAINT and CLOSAT was also proposed. Experimental results demonstrate that in most cases, PSO-tuned CLOSAT and GCAINT produce better results compared to the benchmark methods and able to reduce data sparseness and noise in the dataset

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Iterative Application of the aiNET Algorithm in the Construction of a Radial Basis Function Neural Network

This paper presents some of the procedures adopted in the construction of a Radial Basis Function Neural Network by iteratively applying the aiNET, an Artificial Immune Systems Algorithm. These procedures have shown to be effective in terms of i) the free determination of centroids inspired by an immune heuristics; and ii) the achievement of appropriate minimal square errors after a number of iterations. Experimental and empirical results are compared aiming at confirming (or not) some hypotheses