Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

There has been a rapid growth in using Artificial Immune Systems for applications in data mining and computational intelligence recently. There are extensive computational aspects with the natural immune system. Several algorithms have been developed by exploiting these computational capabilities for a wide range of applications. Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. The results achieved by Artificial Immune Recognition Systems have shown the potential of Artificial Immune Systems to perform classification. Artificial Immune Recognition System is a relatively new classifier and has some advantages such as self regularity, parameter stability and data reduction capability. However, the Artificial Immune Recognition System uses a linear resource allocation method. This linearity increases the processing time of generating memory cells from antigens and causes an increase in the training time of the Artificial Immune Recognition System. Another problem with the Artificial Immune Recognition System is related to the resource competition phase which generates premature memory cells and decreases the classification accuracy of system. This thesis proposes new algorithms based on Artificial Immune Recognition System to address the mentioned weaknesses and improve the performance of the Artificial Immune Recognition System. Firstly, exponential-based resource allocation methods are utilized instead of the existing linear resource allocation method. Next, the Real World Tournament Selection method is adapted and incorporated into the resource competition of Artificial Immune Recognition System. The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. The experimental results show that utilizing exponential-based resource allocation methods decreases the training time and increases the data reduction capability of Artificial Immune Recognition System. In addition, incorporating an adapted Real World Tournament Selection technique increases the accuracy of the Artificial Immune Recognition System up to 4%. The difference between the performances of the proposed algorithms and Artificial Immune Recognition System are significant in majority of cases

Detecting Anomalous Process Behaviour using Second Generation Artificial Immune Systems

Artificial Immune Systems have been successfully applied to a number of problem domains including fault tolerance and data mining, but have been shown to scale poorly when applied to computer intrusion detec- tion despite the fact that the biological immune system is a very effective anomaly detector. This may be because AIS algorithms have previously been based on the adaptive immune system and biologically-naive mod- els. This paper focuses on describing and testing a more complex and biologically-authentic AIS model, inspired by the interactions between the innate and adaptive immune systems. Its performance on a realistic process anomaly detection problem is shown to be better than standard AIS methods (negative-selection), policy-based anomaly detection methods (systrace), and an alternative innate AIS approach (the DCA). In addition, it is shown that runtime information can be used in combination with system call information to enhance detection capability.Comment: 26 pages, 4 tables, 2 figures, International Journal of Unconventional Computin

Web Usage Mining: An Implementation

Web usage mining is the area of data mining which deals with the discovery and analysis of usage patterns from Web data, specifically web logs, in order to improve web based applications. Web usage mining consists of three phases, preprocessing, pattern discovery,and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use these information for the specific needs. In this project, the DSpace log files have been preprocessed to convert the data stored in them into a structured format. Thereafter, the general procedures for bot-removal and session-identification from a web log file, have been written down with certain modifications pertaining to the DSpace log files, in an algorithmic form. Furthermore, analysis of these log files using a subjective interpretation of a recently proposed algorithm EIN-WUM has also been conducted. This algorithm is based on the artificial immune system model and uses this model to learn and extract information present in the web data i.e server logs. This algorithm has been duly modified according to DSpace@NITR Website structure

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

An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

Studies on Real-Valued Negative Selection Algorithms for Self-Nonself Discrimination

The artificial immune system (AIS) is an emerging research field of computational intelligence that is inspired by the principle of biological immune systems. With the adaptive learning ability and a self-organization and robustness nature, the immunology based AIS algorithms have successfully been applied to solve many engineering problems in recent years, such as computer network security analysis, fault detection, and data mining. The real-valued negative selection algorithm (RNSA) is a computational model of the self/non-self discrimination process performed by the T-cells in natural immune systems. In this research, three different real-valued negative selection algorithms (i.e., the detectors with fixed radius, the V-detector with variable radius, and the proliferating detectors) are studied and their applications in data classification and bioinformatics are investigated. A comprehensive study on various parameters that are related with the performance of RNSA, such as the dimensionality of input vectors, the estimation of detector coverage, and most importantly the selection of an appropriate distance metric, is conducted and the figure of merit (FOM) of each algorithm is evaluated using real-world datasets. As a comparison, a model based on artificial neural network is also included to further demonstrate the effectiveness and advantages of RNSA for specific applications