Comparative Analysis of Hybrid Ensemble Methods in Cybersecurity: Integration of Genetic Algorithms and Decision Trees for Intrusion Detection

Abstract

Cybersecurity remains a critical concern in the digital age, with intrusion detection being a pivotal area of research. Conventional methods frequently face challenges in keeping pace with the ever-changing landscape of cyber threats. This paper investigates the efficacy of hybrid ensemble methods combining Genetic Algorithms (GAs) and Decision Trees (DTs) for intrusion detection systems (IDS). Specifically, it evaluates various approaches to integrating GAs with DTs and compares their performance against standalone methods and other ensemble techniques.