Atypical Cyberattack Flow Detection Using Machine Learning Driven Intrusion Detection Systems with Concept Drift Monitoring

Modern cyberattacks increasingly manifest as atypical, low frequency, and adaptive traffic flows that evade traditional signature based intrusion detection systems. Machine learning driven intrusion detection systems have demonstrated strong detection capabilities under static assumptions, yet their performance deteriorates as adversarial behavior, network workloads, and data distributions evolve over time. This study investigates the detection of atypical cyberattack flows through a multi stage intrusion detection architecture that integrates representation learning, ensemble classification, and explicit concept drift monitoring. The proposed framework emphasizes resilience to behavioral shifts while maintaining interpretability and operational stability. Experimental evaluation across heterogeneous attack scenarios demonstrates improved detection robustness, reduced false positives, and sustained performance under evolving traffic conditions.