A Dynamic Intrusion Detection System Integrating Concept Drift Detection and Incremental Learning

The dynamic, evolving, and inherently non-stationary characteristics of modern network environments present a fundamental challenge to traditional intrusion detection systems that rely on static learning paradigms. As network traffic patterns, system usage behaviors, and threat manifestations continuously change over time, the statistical properties underlying detection data are prone to both explicit and implicit variations, commonly described as concept drift. Such drift leads to a gradual mismatch between previously learned models and current data distributions, resulting in performance degradation, delayed responses, and reduced practical effectiveness of fixed detection mechanisms. To address these limitations and support the construction of an intelligent defense system with long-term adaptability, this study conducts a systematic theoretical investigation into dynamic intrusion detection from the perspective of learning evolution. On this basis, a unified framework is proposed that tightly integrates concept drift detection mechanisms with incremental learning strategies, enabling models to identify distributional changes in a timely manner and update their knowledge without retraining from scratch. The framework emphasizes continuity, stability, and adaptability, aiming to balance detection accuracy with computational efficiency under continuously changing conditions. By clarifying the internal relationship between drift detection and incremental model updating, this work provides a structured theoretical foundation for the development of adaptive intrusion detection systems capable of maintaining robust performance in complex and evolving network scenarios.