# On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks

## TL;DR

This paper introduces a machine learning framework for detecting and preventing power jamming attacks in optical networks, demonstrating high detection accuracy and proposing a resource reallocation scheme to mitigate attack success.

## Contribution

It evaluates various ML classifiers for attack detection, identifies the neural network as the most effective, and proposes a novel resource reallocation method for attack prevention.

## Key findings

- Neural network classifier achieves ~100% detection accuracy.
- Detection speed of 10^6 detections per second.
- Resource reallocation reduces jamming success probability.

## 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 detections 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