Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
Xiaoran Liu, Qin Lin, Sicco Verwer, Dmitri Jarnikov
TL;DR
This paper introduces a method for detecting anomalies in digital video broadcasting systems by learning a probabilistic timed automaton model of normal behavior, which is used to identify deviations indicating anomalies.
Contribution
The paper presents a novel approach using probabilistic timed automata to profile and detect anomalies in DVB systems from the provider's perspective.
Findings
Effective detection of anomalies using the learned automaton
High accuracy in identifying non-benign sequences
Applicable to real-time DVB control access monitoring
Abstract
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.
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Advanced Malware Detection Techniques · Algorithms and Data Compression