Special issue on soft computing for information system security

Soft computing, unlike traditional computing, fits prediction models and provides answers to real complex problems. Unlike hard computing, soft computing tolerates great, uncertainty, partial truth and prediction. In fact, the model for soft computing is the human mind. Technologies such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning and expert systems are part of soft computing. The theory of soft computing, introduced in the 1980s, is now a major area of research in all areas. Because of its low cost and high performance, soft computing is now successfully used in many home, commercial and industrial applications. The field of application of soft computing is increasing today. This article outlines the current state of Soft computing technology and explains the latter advantages and disadvantages to traditional computing technology..

As the popularity and usage of Internet increases all over the world, security issues have also become very essential that have to be considered. In literature, different techniques from various disciplines have been utilized to develop efficient security methods but those techniques always have some shortcomings. In this chapter the author discuss soft computing techniques that have been applied in Intrusion Detection System (IDS). The scope of this chapter will encompass core techniques of soft computing including artificial neural networks, Fuzzy Logic Genetic algorithms and hybridization of these approaches. In this chapter the research contribution of each of above mentioned techniques will be systematically summarized and compared that will allows us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this chapter should provide useful insights into the current IDS literature and be a good source for anyone who is...

Biometrics, the computer-based validation of a persons' identity, is becoming more and more essential due to the increasing demand for high-security systems. A biometric system testifies the authenticity of a specific physiological or behavioral characteristic possessed by a user. New requirements over actual biometric systems as robustness, higher recognition rates, tolerance for imprecision and uncertainty, and flexibility call for the use of new computing technologies. In this context soft-computing is increasingly being used in the development of biometric applications. Soft-Biometrics correspond to a new emerging paradigm that consists in the use of soft-computing technologies for the development of biometric applications. The aim of this paper is to motivate discussions on application of soft-computing approaches in specific biometric measurements. The feasibility of soft-computing as a tool-set for biometric applications should be investigated. Finally, an application example on static signature verification is presented, providing evidence of soft-computing's impact in biometrics.

Lecture Notes in Computer Science, 2002

Biometrics, the computer-based validation of a persons' identity, is becoming more and more essential due to the increasing demand for high-security systems. A biometric system testifies the authenticity of a specific physiological or behavioral characteristic possessed by a user. New requirements over actual biometric systems as robustness, higher recognition rates, tolerance for imprecision and uncertainty, and flexibility call for the use of new computing technologies. In this context soft-computing is increasingly being used in the development of biometric applications. Soft-Biometrics correspond to a new emerging paradigm that consists in the use of soft-computing technologies for the development of biometric applications. The aim of this paper is to motivate discussions on application of soft-computing approaches in specific biometric measurements. The feasibility of soft-computing as a tool-set for biometric applications should be investigated. Finally, an application example on static signature verification is presented, providing evidence of soft-computing's impact in biometrics.

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ICRADL – 2021, 2021

Soft Computing can be defined as a science of thinking, reasoning that helps to deal with complex systems. Its main aim is to develop intelligent machines in order to solve realworld problems. It differs from the conventional hard computing as it can handle uncertainty, imprecision easily. It includes use of different techniques such as machine learning, artificial neural networks etc. that can be used together for solving complex problems that are difficult to tackle using conventional models of mathematics. These techniques play a vital role in identifying hidden patterns from the data and doing the classification for making intelligent decisions. This paper reviews some of the soft computing techniques and its applications.

Journal of Intelligent and Fuzzy Systems

2020

Soft computing is a study of the science of logic, thinking, analysis and research that combines real-world problems with biologically inspired methods. Soft computing is the main motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and the resolution of complex problems that are very difficult to model mathematically. Smooth computing tolerates printing; uncertainty and approximation that differ from manual calculation. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques which are used together to solve problems which are complex, very easily. This article highlights the various soft computing ting techniques and emerging areas of soft computing ting where they have been successfully implemented.

2013

In all these forensic applications, soft computing techniques such as neural networks, fuzzy logic, evolutionary computing, and rough set, play an important role in learning complex data structures and patterns, and classifying them to make intelligent decisions. Image forensic techniques use natural properties of image to determine forgery or locate tampering. This special issue aims to highlight state-of-the-art research and novel solutions in information forensic applications using emerging soft computing techniques .The aim of this paper is that with the rise of digital crime (signature forgery, image forgery, illegal transaction, etc.) and the pressing need for methods to combat these forms of criminal activities, there is an increasing awareness of the importance of information forensics for security applications. The emergence and evolution of new digital technologies are dramatically changing how information is captured, processed, analyzed, interpreted, transmitted, and sto...

Soft Computing is the study of science of reasoning, thinking, analyzing and detecting that correlates the real world problems to the biological inspired methods. Soft Computing is the big motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and solve complex problems that too difficult to model mathematically. Soft Computing is tolerant of impression; uncertainty and approximation which is differ from hand computing. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques but when used together solve complex problems very easily. This paper highlights various soft computing techniques and emerging fields of soft computing where they successfully applied.

With the advancement in technology the main issue which exists in every field is security. Biometrics gave solution to this major issue but now a day there is more chances to generate forged biometric traits and also no biometric system is fully secure. So, there is need to generate an efficient biometric system. Biometric system works with the features of biometric traits. Sometimes these features are very large in number which reduces its efficiency. So, to propose a better and efficient biometric system artificial intelligence based soft computing techniques are used to select only best features which will reduce the system's complexity also. This paper discusses various soft computing algorithms and feature selection methods.