Preprint Article Version 1 This version is not peer-reviewed

Machine Learning Techniques to Detecting and Preventing Jamming Attacks in Optical Networks

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. Preprints 2019, 2019010311 (doi: 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 (doi: 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.

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