Cultural Algorithms Unveiled: A Comparative Analysis for Anomaly Detection

Anomaly detection plays a pivotal role in network traffic and intrusion systems. Several Evolutionary Algorithms (EAs) have been employed to address this challenge. This paper presents a comparative analysis of various EAs, including the novel Common Value Auction, utilized in anomaly detection. To ensure a comprehensive comparison, we selected three key features as the basis for evaluating these EAs. This study thoroughly examines and contrasts multiple Evolutionary algorithms based on these features. The proposed analysis evaluates three distinct evolutionary algorithms in terms of three critical aspects of anomaly detection: Robustness, Scalability, and Sensitivity to Parameters. The results indicate that, compared to other evolutionary algorithms, the Common Value Auction evolutionary algorithm exhibits superior robustness, decreased sensitivity, and enhanced flexibility. Consequently, it holds better potential for leverage in anomaly detection.