An intrusion detection system is trained on a dataset of network traffic labeled as normal or malicious. It achieves high accuracy. It is deployed. Over weeks and months, its accuracy degrades — not because the system has changed but because the world has.
New software introduces new protocols. Updated services change normal traffic patterns. Attackers develop new techniques. The classifier's training data becomes a snapshot of a world that no longer exists. The concept — what “normal” and “malicious” mean in practice — has drifted out from under the model.
The paradox: the infrastructure is more stable than the model trained on it. The servers, the network topology, the basic protocols change slowly. But the statistical distribution of traffic changes fast — seasonality, new applications, evolving user behavior, and adversarial adaptation all modify what the classifier sees. A model of a stable system degrades because the system's behavior is not the system's structure.
Lifelong learning addresses this by continuously updating the model on new data, retraining on recent traffic while retaining knowledge of old patterns. But retention conflicts with adaptation: remembering old attack signatures may prevent learning new normal patterns if the feature distributions overlap.
The structural point: the stability of the thing being modeled does not imply stability of the model. Infrastructure can be constant while traffic distributions shift. The concept that the model learned — a statistical boundary between normal and malicious — is a function of the deployment time, not of the underlying system. Concept drift is not a bug in the model but a fact about the relationship between structure and statistics.