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Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Optical Network Laboratory (ON Lab).ORCID iD: 0000-0001-5600-3700
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Optical Network Laboratory (ON Lab).ORCID iD: 0000-0001-7501-5547
Telecom Italia, Turin, Italy..
Telecom Italia, Turin, Italy..
2019 (English)In: METRO AND DATA CENTER OPTICAL NETWORKS AND SHORT-REACH LINKS II / [ed] Srivastava, AK Glick, M Akasaka, Y, SPIE-INT SOC OPTICAL ENGINEERING , 2019, article id 109460DConference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
SPIE-INT SOC OPTICAL ENGINEERING , 2019. article id 109460D
Series
Proceedings of SPIE, ISSN 0277-786X ; 10946
Keywords [en]
Optical network security, dataset exploration, data analytics, unsupervised learning, anomaly detection
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-259466DOI: 10.1117/12.2509613ISI: 000483011800010Scopus ID: 2-s2.0-85068262171ISBN: 978-1-5106-2535-8 (print)OAI: oai:DiVA.org:kth-259466DiVA, id: diva2:1352859
Conference
Conference on Metro and Data Center Optical Networks and Short-Reach Links II, FEB 05-06, 2019, San Francisco, CA
Note

QC 20190920

Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-09-20Bibliographically approved

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Furdek, MarijaNatalino, Carlos

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