Version 1
: Received: 29 January 2019 / Approved: 30 January 2019 / Online: 30 January 2019 (10:16:14 CET)
Version 2
: Received: 17 June 2019 / Approved: 18 June 2019 / Online: 18 June 2019 (07:26:54 CEST)
How to cite:
Bensalem, M.; Singh, S.K.; Jukan, A. On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks. Preprints2019, 2019010311. https://doi.org/10.20944/preprints201901.0311.v2
Bensalem, M.; Singh, S.K.; Jukan, A. On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks. Preprints 2019, 2019010311. https://doi.org/10.20944/preprints201901.0311.v2
Bensalem, M.; Singh, S.K.; Jukan, A. On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks. Preprints2019, 2019010311. https://doi.org/10.20944/preprints201901.0311.v2
APA Style
Bensalem, M., Singh, S.K., & Jukan, A. (2019). On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks. Preprints. https://doi.org/10.20944/preprints201901.0311.v2
Chicago/Turabian Style
Bensalem, M., Sandeep Kumar Singh and Admela Jukan. 2019 "On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks" Preprints. https://doi.org/10.20944/preprints201901.0311.v2
Abstract
Optical networks are prone to power jamming attacks intending service disruption. This paper presents a Machine Learning (ML) framework for detection and prevention of jamming attacks in optical networks. We evaluate various ML classifiers for detecting out-of-band jamming attacks with varying intensities. Numerical results show that artificial neural network is the fastest ($10^6$ detection per second) for inference and most accurate ($\approx 100 \%$) in detecting power jamming attacks as well as identifying the optical channels attacked. We also discuss and study a novel prevention mechanism when the system is under active jamming attacks. For this scenario, we propose a novel resource reallocation scheme that utilizes the statistical information of attack detection accuracy to lower the probability of successful jamming of lightpaths while minimizing lightpaths' reallocations. Simulation results show that the likelihood of jamming a lightpath reduces with increasing detection accuracy, and localization reduces the number of reallocations required.
Keywords
optical networks; jamming attacks; machine learning; detection and prevention; routing and spectrum assignment; security
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
