The main goal in this paper is to design single-mode optical fibers for DWDM networks, which are used today in rapid communications. These networks require low dispersion in a wide range of wavelengths. So, in this paper, multi-layer optical fibers with low dispersion value and flat dispersion slope in wavelength range of 1.5-1.6µm) are designed, using optimization algorithms.
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.
On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks
