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Experimental Study ...
Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks
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- Natalino Da Silva, Carlos, 1987 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Schiano, Marco (author)
- Telecom Italia S.P.A
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- Di Giglio, Andrea (author)
- Telecom Italia S.P.A
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- Wosinska, Lena, 1951 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Furdek Prekratic, Marija, 1985 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Chalmers tekniska högskola Telecom Italia SP.A (creator_code:org_t)
- 2019
- 2019
- English.
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In: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 37:16, s. 4173-4182
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
Keyword
- monitoring
- machine learning
- optical network security
- attack detection
Publication and Content Type
- art (subject category)
- ref (subject category)
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