Full Program
Summary:
In the evolving digital landscape, unprecedented opportunities arise, but also cyber threats grow in scale and sophistication. Critical infrastructures face challenges in maintaining operational resilience and security. Traditional mitigation approaches often fall short in dynamic environments, where rapid, cost-effective, and context-tailored responses are required. Towards finding a way out of this predicament, this paper introduces a multi-objective optimization framework designed to enhance cybersecurity decision-making by identifying optimal mitigation strategies for specific cyber-attack scenarios. Leveraging an evolutionary algorithm, named Non-dominated Sorting Genetic Algorithm-II (NSGA-II) , the framework models mitigation selection as a Pareto optimization problem, balancing conflicting objectives, such as effectiveness, relevance, response time, and cost.Author(s):
Konstantina Milousi
Greece
Nikolaos Vakakis
Greece
Aristeidis Mystakidis
Greece
Mariana S. Mazi
Greece
Antonis Voulgaridis
Greece
Christos Tjortjis
Greece
Konstantinos Votis
Greece
Dimitrios Tzovaras
Greece