Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Experiment-based detection of service disruption attacks in optical networks using data analytics and unsupervised learning
KTH, Skolan för elektroteknik och datavetenskap (EECS), Kommunikationssystem, CoS, Optical Network Laboratory (ON Lab).ORCID-id: 0000-0001-5600-3700
KTH, Skolan för elektroteknik och datavetenskap (EECS), Kommunikationssystem, CoS, Optical Network Laboratory (ON Lab).ORCID-id: 0000-0001-7501-5547
Telecom Italia, Turin, Italy..
Telecom Italia, Turin, Italy..
2019 (Engelska)Ingår i: METRO AND DATA CENTER OPTICAL NETWORKS AND SHORT-REACH LINKS II / [ed] Srivastava, AK Glick, M Akasaka, Y, SPIE-INT SOC OPTICAL ENGINEERING , 2019, artikel-id 109460DKonferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
SPIE-INT SOC OPTICAL ENGINEERING , 2019. artikel-id 109460D
Serie
Proceedings of SPIE, ISSN 0277-786X ; 10946
Nyckelord [en]
Optical network security, dataset exploration, data analytics, unsupervised learning, anomaly detection
Nationell ämneskategori
Kommunikationssystem
Identifikatorer
URN: urn:nbn:se:kth:diva-259466DOI: 10.1117/12.2509613ISI: 000483011800010Scopus ID: 2-s2.0-85068262171ISBN: 978-1-5106-2535-8 (tryckt)OAI: oai:DiVA.org:kth-259466DiVA, id: diva2:1352859
Konferens
Conference on Metro and Data Center Optical Networks and Short-Reach Links II, FEB 05-06, 2019, San Francisco, CA
Anmärkning

QC 20190920

Tillgänglig från: 2019-09-20 Skapad: 2019-09-20 Senast uppdaterad: 2019-09-20Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopusProceedings

Personposter BETA

Furdek, MarijaNatalino, Carlos

Sök vidare i DiVA

Av författaren/redaktören
Furdek, MarijaNatalino, Carlos
Av organisationen
Optical Network Laboratory (ON Lab)
Kommunikationssystem

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 18 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf