Intrusion Detection Using the Combinationof Two Meta-Heuristic Algorithms IncludingIntelligent Water Drops and Particle SwarmOptimization

As technology continues to advance, becoming increasingly intertwined withcommunication, the importance of safeguarding exchanged data has become more criticalthan ever. The relentless efforts of attackers and cyber hackers to infiltrate computernetworks and manipulate systems have accentuated the need for heightened securitymeasures. Consequently, numerous researchers have delved into extensive research tocombat these threats. In this research paper, a novel approach utilizing a combinedmetaheuristic method employing Intelligent Water Drops (IWD) and Particle SwarmOptimization (PSO) is introduced. The incorporation of IWD serves to eliminateineffective features in attack detection, thereby discarding those attributes that maycompromise accuracy. Moreover, the utilization of PSO facilitates the execution of theclassification operation, a pivotal phase in the process of attack detection. The resultsobtained from simulations demonstrate remarkable classification accuracies of ۹۹.۹۱% inbinary mode and ۹۹.۸۸% in multiclass mode, underscoring the superior efficacy of theproposed methodology in comparison to alternative approaches put forth in the field.

Keywords:

Intrusion Detection , Intelligent Water Drops , Particle Swarm Optimization , Feature Selection , Metaheuristic Algorithm