Experimentation with a back-propagation neural network
Information and Management
Neural network models for intelligent support of managerial decision making
Decision Support Systems - Special issue on neural networks for decision support
Integrating artificial neural networks with rule-based expert systems
Decision Support Systems - Special issue on neural networks for decision support
Decision support in non-conservative domains: generalization with neural networks
Decision Support Systems - Special issue on neural networks for decision support
Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Portfolios of Control in Outsourced Software Development Projects
Information Systems Research
Why there aren't more information security research studies
Information and Management
An integrative model of computer abuse based on social control and general deterrence theories
Information and Management
Deploying Common Systems Globally: The Dynamics of Control
Information Systems Research
Investigating factors affecting the adoption of anti-spyware systems
Communications of the ACM - Spyware
The impact of EDI controls on EDI implementation
International Journal of Electronic Commerce
Threats and countermeasures for information system security: A cross-industry study
Information and Management
International Journal of Electronic Commerce
A hybrid model using genetic algorithm and neural network for classifying garment defects
Expert Systems with Applications: An International Journal
Using case-based reasoning for the design of controls for internet-based information systems
Expert Systems with Applications: An International Journal
Research of multi-population agent genetic algorithm for feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Decision Support Systems - Special issue: Intelligence and security informatics
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
An integrative study of information systems security effectiveness
International Journal of Information Management: The Journal for Information Professionals
Expert Systems with Applications: An International Journal
International Journal of Business Information Systems
Hi-index | 12.05 |
As organizations become increasingly dependent on Internet-based systems for business-to-consumer electronic commerce (ISB2C), the issue of IS security becomes increasingly important. As the usage of security controls is related to the implementation of ISB2C, the extent of ISB2C controls can be adjusted in order to enable the greatest extent of implementation of ISB2C. This study intends to propose ISB2C-NNGA (ISB2C-controls design using neural networks and genetic algorithms), a hybrid optimization model using neural networks and genetic algorithms for the design of ISB2C controls, which uses back-propagation neural networks (BPN) model as a prediction of controls using system environments, and GA as a pattern directed search mechanism to estimate the exponent of independent variables (i.e., ISB2C controls) in multivariate regression analysis of power model. The effect of system environments on controls can be estimated using BPN model which outperformed linear regression analysis in terms of square root of mean squared error. The effect of each mode of controls on implementation (volume) can be identified using exponents and standardized coefficients in the GA-based nonlinear regression analysis in ISB2C-NNGA. ISB2C-NNGA outperformed conventional linear regression analysis in prediction accuracy in terms of the average R square and sum of squared error. ISB2C can suggest the best set of values for controls to be recommended from several candidate sets of values for controls by identifying the set of values for controls which produce greatest extent of ISB2C implementation. The results of study will support the design of ISB2C controls effectively.