scholarly journals A New Approach to Artificial Immune Systems and its Application in Constructing On-line Learning Neuro-Fuzzy Systems

2008 ◽  
Vol 2 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Mu-Chun Su ◽  
Po-Chun Wang ◽  
Yuan-Shao Yang
Keyword(s):  
Fuzzy Systems ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
New Approach ◽  
Immune Systems ◽  
Neuro Fuzzy ◽  
On Line ◽  
On Line Learning

scholarly journals Reinforcement Learning Based Artificial Immune Classifier

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mehmet Karakose
Keyword(s):  
Reinforcement Learning ◽  
Artificial Immune Systems ◽  
Image Data ◽  
Memory Cell ◽  
Real Data ◽  
Experimental Results ◽  
Natural Immunity ◽  
Artificial Immune ◽  
New Approach ◽  
Immune Systems

One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

Applications of Computational Intelligence in Computing Security

2020 ◽  
pp. 1-22
Author(s):  
Yousif Abdullatif Albastaki
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Swarm Intelligence ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems

This chapter is an introductory chapter that attempts to highlight the concept of computational intelligence and its application in the field of computing security; it starts with a brief description of the underlying principles of artificial intelligence and discusses the role of computational intelligence in overcoming conventional artificial intelligence limitations. The chapter then briefly introduces various tools or components of computational intelligence such as neural networks, evolutionary computing, swarm intelligence, artificial immune systems, and fuzzy systems. The application of each component in the field of computing security is highlighted.

scholarly journals METHOD OF SPAM DETECTION BASED ON ARTIFICIAL IMMUNE SYSTEMS

2018 ◽  
pp. 38-48
Author(s):  
Maria Petrovna Malykhina ◽  
Vera Arkadyevna Chastikova ◽  
Alexandr Aleksandrovich Biktimirov
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Low Frequency ◽  
Spam Detection ◽  
Artificial Immune ◽  
New Approach ◽  
Immune Systems ◽  
Initial Stage ◽  
Living Organisms

The task of developing tools to combat spam is currently focused on creating such techniques for detecting spam, which are endowed with the skills and qualities inherent in a person whose work is not limited to patterns and therefore highly effective. Man has the ability to detect spam signs, which is based on his own knowledge, experience and preferences. There has been substantiated the need to develop a new approach to solving the problem of detecting spam messages, which is based on heuristic methods of optimization, is effective at the initial stage of training and has a low frequency of false operations. This formulation of the problem fully corresponds to modeling mechanisms of the immune systems of living organisms that ensure their survival, these mechanisms being represented, investigated and used by software. There have been identified and described main mechanisms of artificial immune systems intended for solving the problem of spam detection, as well as software and system interacting. The basic concepts of constructing an artificial immune system for the purpose formulated above are determined: class of detectors, presentation of receptors and pathogens. A model of the relationships between them has been worked out. A technique for detecting spam based on the work of an artificial immune system is proposed, an algorithm for its implementation is developed, and the specifics of its members to identify spam messages are described. A software package with advanced research capabilities has been created. Testing and analysis of the results to determine the optimum values of the system operation parameters have been conducted.

Developing an Algorithm for the Application of Bayesian Method to Software Using Artificial Immune Systems

2021 ◽  
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Bayesian Method ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Computational System ◽  
Malicious Software ◽  
Immune Systems ◽  
Advantages And Disadvantages ◽  
Protection Systems

Abstract This study provides information on artificial immune systems. The artificial immune system is an adaptive computational system that uses models, principles, mechanisms and functions to describe and solve the problems in theoretical immunology. Its application in various fields of science is explored. The theory of natural immune systems and the key features and algorithms of artificial immune system are analyzed. The advantages and disadvantages of protection systems based on artificial immune systems are shown. The methods for malicious software detection are studied. Some works in the field of artificial immune systems are analyzed, and the problems to be solved are identified. A new algorithm is developed for the application of Bayesian method in software using artificial immune systems, and experiments are implemented. The results of the experiment are estimated to be good. The advantages and disadvantages of AIS were shown. To eliminate the disadvantages, perfect AISs should be developed to enable the software more efficient and effective.

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