Adaptive systems are systems whose function evolves while adapting to current environmental conditions. Due to the real-time adaptation, newly learned data have a significant impact on system behavior. When online adaptation is included in system control, anomalies could cause abrupt loss of system functionality and possibly result in a failure.

In this paper we present a framework for reasoning about the online adaptation problem. We describe a machine learning tool that sniffs data and detects anomalies before they are passed to the adaptive components for learning. Anomaly detection is based on distance computation. An algorithm for framework evaluation as well as sample implementation and empirical results are discussed. The method we propose is simple and reasonably effective, thus it can be easily adopted for testing.