Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
Machine Learning
2017-05-29 v1 Artificial Intelligence
Formal Languages and Automata Theory
Logic in Computer Science
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.
Keywords
Cite
@article{arxiv.1705.09650,
title = {Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata},
author = {Xiaoran Liu and Qin Lin and Sicco Verwer and Dmitri Jarnikov},
journal= {arXiv preprint arXiv:1705.09650},
year = {2017}
}
Comments
This paper has been accepted by the Thirty-Second Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) Workshop on Learning and Automata (LearnAut)