Hierarchical classification for intrusion detection system: Effective design and empirical analysis

The growing adoption of network technologies, particularly the Internet of Things (IoT), has led to the emergence of new and increasingly complex cyberattacks. To protect critical infrastructure from these evolving threats, it is essential to implement Intrusion Detection Systems (IDS) capable of accurately detecting a wide range of attacks while minimizing false alarms. While machine learning has been widely applied in IDS, most approaches rely on flat multi-class classification to distinguish between normal traffic and various attack types. However, cyberattacks often exhibit a hierarchical structure, where granular attack subtypes can be grouped under broader high-level categories—an aspect largely underexplored in IDS research. In this paper, we investigate the effectiveness of hierarchical classification in the context of IDS. We propose a…