Machine learning-based detection, identification and restoration method of jamming attacks in optical networks

Abstract

Optical networks are vulnerable to signal jamming attacks aimed at disrupting communication services due to their structural fragility.Based on this, a machine learning-based jamming attacks detection, identification and restoration framework was proposed.In terms of attacks detection and identification, the performances of BiLSTM, 1DCNN, and seven conventional machine learning classifiers (ANN, DT, KNN, LDA, NB, RF, and SVM) were evaluated in detecting the presence of attacks, and identifying different types of jamming attacks.In terms of attacks restoration, a BiLSTM-BiGRU-based jamming attacks restoration model was proposed to restore light-in-band, strong-in-band, light-out-of-band, and strong-out-of-band jamming attacks.Numerical simulation results reveal that the proposed model demonstrates excellent performance with a detection and identification accuracy of 99.20%, with attack restoration ratios of 95.05%, 97.03%, 94.06%, and 61.88%, respectively.

Keywords

• machine learning
• attack detection and identification
• attack recovery
• optical network security