We describe the implementation and performance of a compression-based model inference engine, MDLcompress. The MDL-based compression produces a two part code of the training data, with the model portion of the code being used to compress and classify test data. We present pseudo-code of the algorithms for model generation and explore the conflicting requirements between minimizing grammar size and minimizing descriptive cost. We show results of a MDL model-based classification system for network traffic anomaly detection.

DiffServ offers an attractive solution to quality of service (QoS) for mobile ad-hoc networks (MANET)s since the overhead of alternative flow based QoS metrics and signaling is not required. However, within prioritized classes and in the presence of dynamically forming bottlenecks, DiffServ can lead to brittle failure modes in which all flows are highly penalized and subsequently fail rather than maintaining some flows at required QoS levels for latency or packet loss. This problem is particularly difficult for UDP traffic, which does not respond to random early detection (RED) like throttling mechanisms in the presence of congestion. This paper proposes an augmentation of DiffServ QoS over MANET that utilizes metrics available through routing protocols to prevent, and resolve congestion. This is done in a manner that promotes maintenance of some high priority UDP (for example voice over IP) flows in the presence of bottlenecks. The biased early drop (BED) algorithm is introduced to maintain high continuity of UDP flows in the presence of congestion within the same class of service.