J. Wang, X.Y. Li, J. Hu, P. Zhang, and G.Q. Gong (PRC)
Improved PSO, GA, Information Security Investment
Organizations are determining the optimal amount to invest to protect a given set of information system from the threat presented by vulnerabilities. The decisions concerning information security investment is a multi objective problem and it is NP-hard to solve exactly. The objective of this article is to present and evaluate an improved binary particle swarm optimization (PSO) based approach enabling organizations to choose the minimal-cost security investment schemes with the maximal vulnerability coverage. To speed up the convergence, the memory mechanism is implanted in the traditional binary PSO algorithm. After experiment, the proposed algorithm has demonstrated higher searching efficiency and better stability than the genetic algorithms mentioned in other literatures. The PSO-based approach provides favorable results and a simple tool for supporting information security investment decisions making.
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