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 (≈ 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.

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Optical networks are prone to physical layer attacks, in particular the insertion of high jamming power. In this paper, we present a study of jamming attacks in elastic optical networks (EON) by embedding the jamming into the physical layer model, and we analyze its impact on the blocking probability and slots utilization. We evaluate our proposed model using a single link and a network topology and we show that for in-band-jamming, the slots utilization decreases with the increase of jamming power, and becomes null when the jamming power is higher than 3 dB, while for out-of-band jamming, the impact is maximal for a specific jamming power, 1.75 dB in our simulation. Considering multiple positions of attackers, we attained the highest blocking probability 32% for a specific jamming power 2 dB. We conclude that the impact of jamming depends on attacker positions as well as the jamming power.