One step ahead to multisensor data fusion for DDoS detection

摘要

This work introduces the use of data fusion in the field of DDoS anomaly detection. We present Dempster-Shafer Theory of Evidence (D-S), the mathematical foundation for the development of a novel DDoS detection engine. Based on a data fusion paradigm, we combine evidence generated from multiple simple metrics to feed our D-S inference engine and detect attacks on a single network element (high bandwidth link). The main advantages of our approach are the modeling power of the Theory of Evidence in expressing beliefs in some hypotheses, its flexibility to handle uncertainty and ignorance and its ability to provide quantitative measurement of the belief and plausibility in our detection results. Furthermore we propose a system that can be trained (supervised learning) with minimum human effort, using in parallel expert knowledge about each metric detection ability. We evaluate our detection engine prototype through an extensive set of experiments on an operational network using real network traffic, with the use of a popular DDoS attack generator. Based on these results we discuss the performance of our D-S scheme in contrast to simple thresholds on single metrics, as well as against an alternative data fusion technique based on an Artificial Neural Network. We conclude that our data fusion is a promising approach that significantly increases the DDoS detection rate (true positives) while keeping the false positive alarm rate low.