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Computational Intelligence Paradigms: An Overview

Computational Intelligence Paradigms: An Overview
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Author(s): El-Sayed M. El-Alfy (King Fahd University of Petroleum and Minerals, Saudi Arabia) and Wasan Awad (Ahlia University, Bahrain)
Copyright: 2016
Pages: 27
Source title: Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0159-6.ch002

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Abstract

The evolution of communication networks and information systems, to support wireless access, cloud and grid computing, and big data, provides great business opportunities. However, it also generates a new trend of sophisticated network threats and offers several challenges in securing information and systems confidentiality, integrity and availability. The traditional techniques used by security experts are mostly static and lack the much needed characteristics of adaptation and self-organization, computational efficiency and error resilience to deal with evolving attacks. The inherent characteristics of computational intelligence (CI) paradigms provide a promising alternative that has gained popularity resulting in significant applications in information security. There is a plethora of CI paradigms commonly used in this domain including artificial neural networks, evolutionary computing, fuzzy systems, and swarm intelligence. This chapter provides an overview of the widely-recognized CI paradigms and shades the light on some of their potential applications in information security.

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