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Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning
Author(s): Marija Furdek; Carlos Natalino; Marco Schiano; Andrea Di Giglio
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Paper Abstract

The paper addresses the detection of malicious attacks targeting service disruption at the optical layer as a key prerequisite for fast and effective attack response and network recovery. We experimentally demonstrate the effects of signal insertion attacks with varying intensity in a real-life scenario. By applying data analytics tools, we analyze the properties of the obtained dataset to determine how the relationships among different optical performance monitoring (OPM) parameters of the signal change in the presence of an attack as opposed to the normal operating conditions. In addition, we evaluate the performance of an unsupervised learning technique, i.e., a clustering algorithm for anomaly detection, which can detect attacks as anomalies without prior knowledge of the attacks. We demonstrate the potential and the challenges of unsupervised learning for attack detection, propose guidelines for attack signature identification needed for the detection of the considered attack methods, and discuss remaining challenges related to optical network security.

Paper Details

Date Published: 1 February 2019
PDF: 10 pages
Proc. SPIE 10946, Metro and Data Center Optical Networks and Short-Reach Links II, 109460D (1 February 2019); doi: 10.1117/12.2509613
Show Author Affiliations
Marija Furdek, KTH Royal Institute of Technology (Sweden)
Carlos Natalino, KTH Royal Institute of Technology (Sweden)
Marco Schiano, Telecom Italia (Italy)
Andrea Di Giglio, Telecom Italia (Italy)


Published in SPIE Proceedings Vol. 10946:
Metro and Data Center Optical Networks and Short-Reach Links II
Atul K. Srivastava; Madeleine Glick; Youichi Akasaka, Editor(s)

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