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. Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks. Preprints2019, 2019010311. https://doi.org/10.20944/preprints201901.0311.v1
Bensalem, M.; Singh, S.K.; Jukan, A. Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks. Preprints 2019, 2019010311. https://doi.org/10.20944/preprints201901.0311.v1
Bensalem, M.; Singh, S.K.; Jukan, A. Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks. Preprints2019, 2019010311. https://doi.org/10.20944/preprints201901.0311.v1
APA Style
Bensalem, M., Singh, S.K., & Jukan, A. (2019). Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks. Preprints. https://doi.org/10.20944/preprints201901.0311.v1
Chicago/Turabian Style
Bensalem, M., Sandeep Kumar Singh and Admela Jukan. 2019 "Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks" Preprints. https://doi.org/10.20944/preprints201901.0311.v1
Abstract
We study the effectiveness of various machine learning techniques, including artificial neural networks, support vector machine, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian, for detecting and mitigating power jamming attacks in optical networks. Our study shows that artificial neural network is the most accurate in detecting out-of-band power jamming attacks in optical networks. To further mitigating the power jamming attacks, we apply a new resource reallocation scheme that utilizes the statistical information of attack detection accuracy, and propose a resource reallocation algorithm to lower the probability of successful jamming of lightpaths. Simulation results show that higher the accuracy of detection, lower is the likelihood of jamming a lightpath.
Keywords
No keywords defined for manuscript.
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.
