Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
This addresses anomaly detection for DVB system providers, but it appears incremental as it applies an existing timed automaton method to a specific domain.
The paper tackled anomaly detection in digital video broadcasting systems by learning a probabilistic deterministic real timed automaton to profile benign behavior, using it as a one-class classifier to detect anomalous sequences not accepted by the model.
This paper focuses on detecting anomalies in a digital video broadcasting (DVB) system from providers' perspective. We learn a probabilistic deterministic real timed automaton profiling benign behavior of encryption control in the DVB control access system. This profile is used as a one-class classifier. Anomalous items in a testing sequence are detected when the sequence is not accepted by the learned model.