  Intrusion Detection Systems (IDS) are now an essential element when it comes
to securing computers and networks. Despite the huge research efforts done in
the field, handling sources' reliability remains an open issue. To address this
problem, this paper proposes a novel contextual discounting method based on
sources' reliability and their distinguishing ability between normal and
abnormal behavior. Dempster-Shafer theory, a general framework for reasoning
under uncertainty, is used to construct an evidential classifier. The NSL-KDD
dataset, a significantly revised and improved version of the existing KDDCUP'99
dataset, provides the basis for assessing the performance of our new detection
approach. While giving comparable results on the KDDTest+ dataset, our approach
outperformed some other state-of-the-art methods on the KDDTest-21 dataset
which is more challenging.
