Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks
Artikel i vetenskaplig tidskrift, 2019

Optical networks are critical infrastructure supporting vital services and are vulnerable to different types of malicious attacks targeting service disruption at the optical layer. Due to the various attack techniques causing diverse physical- layer effects, as well as the limitations and sparse placement of optical performance monitoring devices, such attacks are difficult to detect, and their signatures are unknown. This paper presents a Machine Learning (ML) framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field-deployed testbed with coherent receivers. We perform in-band and out-of-band jamming signal insertion attacks, as well as polarization modulation attacks, each with varying intensities. We then evaluate 8 different ML classifiers in terms of their accuracy, and scalability in processing experimental data. The optical parameters critical for accurate attack identification are identified and the generalization of the models is validated. Results indicate that Artificial Neural Networks (ANNs) achieve 99.9% accuracy in attack type and intensity classification, and are capable of processing 1 million samples in less than 10 seconds.

monitoring

attack detection

optical network security

machine learning

Författare

Carlos Natalino Da Silva

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Marco Schiano

Telecom Italia S.P.A

Andrea Di Giglio

Telecom Italia S.P.A

Lena Wosinska

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Marija Furdek Prekratic

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Journal of Lightwave Technology

0733-8724 (ISSN)

Vol. 37 16 4173-4182

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Telekommunikation

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

DOI

10.1109/JLT.2019.2923558

Mer information

Senast uppdaterat

2019-10-30