An Adaptive Anomaly Detector for Worm Detection

We present an adaptive end-host anomaly detector where a supervised classifier trained as a traffic predictor is used to control a time-varying detection threshold. Using real enterprise traffic traces for both training and testing, we show that our detector outperforms a fixed-threshold detector. This comparison is robust to the choice of off-the-shelf classifier and to a variety of performance criteria, i.e., the predictor's error rate, the reduction in the "threshold gap," and the ability to detect incremental worm traffic that is added to real life traces. Our adaptive-threshold detector is intended as a part of a distributed worm detection system. This distributed system infers system-wide threats from end-host detections, thereby avoiding the sensing and resource limitations of conventional centralized systems. The system places a constraint on this end-host detector to appear consistent over time and host variability