MOMENT: A Multi-Objective Mitigation Engine using NSGA-II Techniques for Cyber Threat Response

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 Multi-Objective Mitigation Engine using NSGA-II Techniques (MOMENT) 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), MOMENT models mitigation selection as a Pareto optimization problem, balancing conflicting objectives, such as effectiveness, relevance, response time, and cost. A matrix-based scoring system is employed to rigorously evaluate potential actions, enabling the algorithm to uncover trade-offs and generate a diverse set of Paretooptimal solutions. Experimental results demonstrate MOMENT’s ability to support automated, adaptive, and resource-efficient threat response, offering a practical tool for securing critical infrastructures in complex and evolving threat landscapes.