Optimizing Network Vulnerability Scanning Using Adaptive Evolutionary Algorithms

Abstract

Network vulnerability scanning is crucial for maintaining the security of computer networks. Traditional scanning methods often suffer from inefficiencies in terms of resource utilization and time. This paper explores the application of adaptive evolutionary algorithms to optimize network vulnerability scanning processes. Specifically, genetic algorithms (GAs) and other evolutionary techniques are employed to enhance the efficiency and effectiveness of vulnerability assessments. The study focuses on adapting scanning parameters dynamically to match network conditions and threat landscapes, thereby improving overall security posture and reducing operational overhead.