A graph-based system for network-vulnerability analysis
Proceedings of the 1998 workshop on New security paradigms
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Security attribute evaluation method: a cost-benefit approach
Proceedings of the 24th International Conference on Software Engineering
Scalable, graph-based network vulnerability analysis
Proceedings of the 9th ACM conference on Computer and communications security
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The coras approach for model-based risk management applied to e-commerce domain
Proceedings of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security: Advanced Communications and Multimedia Security
Two Formal Analys s of Attack Graphs
CSFW '02 Proceedings of the 15th IEEE workshop on Computer Security Foundations
Automated Generation and Analysis of Attack Graphs
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Efficient Minimum-Cost Network Hardening Via Exploit Dependency Graphs
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
International Journal of Intelligent Systems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Optimal security hardening using multi-objective optimization on attack tree models of networks
Proceedings of the 14th ACM conference on Computer and communications security
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Decision Support Systems - Special issue: Intelligence and security informatics
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Scale-free fully informed particle swarm optimization algorithm
Information Sciences: an International Journal
Using attack trees to identify malicious attacks from authorized insiders
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Optimal security hardening on attack tree models of networks: a cost-benefit analysis
International Journal of Information Security
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
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In particle swarm optimization (PSO) each particle uses its personal and global or local best positions by linear summation. However, it is very time consuming to find the global or local best positions in case of complex problems. To overcome this problem, we propose a new multi-objective variant of PSO called attributed multi-objective comprehensive learning particle swarm optimizer (A-MOCLPSO). In this technique, we do not use global or local best positions to modify the velocity of a particle; instead, we use the best position of a randomly selected particle from the whole population to update the velocity of each dimension. This method not only increases the speed of the algorithm but also searches in more promising areas of the search space. We perform an extensive experimentation on well-known benchmark problems such as Schaffer (SCH), Kursawa (KUR), and Zitzler-Deb-Thiele (ZDT) functions. The experiments show very convincing results when the proposed technique is compared with existing versions of PSO known as multi-objective comprehensive learning particle swarm optimizer (MOCLPSO) and multi-objective particle swarm optimization (MOPSO), as well as non-dominated sorting genetic algorithm II (NSGA-II). As a case study, we apply our proposed A-MOCLPSO algorithm on an attack tree model for the security hardening problem of a networked system in order to optimize the total security cost and the residual damage, and provide diverse solutions for the problem. The results of our experiments show that the proposed algorithm outperforms the previous solutions obtained for the security hardening problem using NSGA-II, as well as MOCLPSO for the same problem. Hence, the proposed algorithm can be considered as a strong alternative to solve multi-objective optimization problems.