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In this article, we propose CANDIES (Combined Approach for Novelty Detectionin Intelligent Embedded Systems), a new approach to novelty detection intechnical systems. We assume that in a technical system several processesinteract. If we observe these processes with sensors, we are able to model theobservations (samples) with a probabilistic model, where, in an ideal case, thecomponents of the parametric mixture density model we use, correspond to theprocesses in the real world. Eventually, at run-time, novel processes emerge inthe technical systems such as in the case of an unpredictable failure. As aconsequence, new kinds of samples are observed that require an adaptation ofthe model. CANDIES relies on mixtures of Gaussians which can be used forclassification purposes, too. New processes may emerge in regions of themodels' input spaces where few samples were observed before (low-densityregions) or in regions where already many samples were available (high-densityregions). The latter case is more difficult, but most existing solutions focuson the former. Novelty detection in low- and high-density regions requiresdifferent detection strategies. With CANDIES, we introduce a new technique todetect novel processes in high-density regions by means of a fast onlinegoodness-of-fit test. For detection in low-density regions we combine thisapproach with a 2SND (Two-Stage-Novelty-Detector) which we presented inpreliminary work. The properties of CANDIES are evaluated using artificial dataand benchmark data from the field of intrusion detection in computer networks,where the task is to detect new kinds of attacks.

Detecting Novel Processes with CANDIES -- An Holistic Novelty Detection Technique based on Probabilistic Models