Introduction to algorithms
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SP 800-33. Underlying Technical Models for Information Technology Security
SP 800-33. Underlying Technical Models for Information Technology Security
Multiobjective Evolutionary Clustering Approach to Security Vulnerability Assesments
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Particle swarm optimization approach for information security investment decision
CA '07 Proceedings of the Ninth IASTED International Conference on Control and Applications
Toward user patterns for online security: Observation time and online user identification
Decision Support Systems
Expert Systems with Applications: An International Journal
Dynamic deployment of context-aware access control policies for constrained security devices
Journal of Systems and Software
Analysis of vulnerability assessment results based on CAOS
Applied Soft Computing
Selection of optimal countermeasure portfolio in IT security planning
Decision Support Systems
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Organizations are making substantial investments in information security to reduce the risk presented by vulnerabilities in their information technology (IT) infrastructure. However, each security technology only addresses specific vulnerabilities and potentially creates additional vulnerabilities. The objective of this research is to present and evaluate a Genetic Algorithm (GA)-based approach enabling organizations to choose the minimal-cost security profile providing the maximal vulnerability coverage. This approach is compared to an enumerative approach for a given test set. The GA-based approach provides favorable results, eventually leading to improved tools for supporting information security investment decisions.