基于机器学习的光网络干扰攻击检测、识别与恢复方法

doi:10.11959/j.issn.1000-436x.2023127

Abstract

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.

Key words: machine learning, attack detection and identification, attack recovery, optical network security

CLC Number:

TN913.7

Xiaoxue GONG, Jiahao PANG, Qihan ZHANG, Changle XU, Wenshuai QIN, Lei GUO. Machine learning-based detection, identification and restoration method of jamming attacks in optical networks[J]. Journal on Communications, 2023, 44(7): 159-170.

Figures/Tables 19

参数	值
波特率/GBaud	25
发射光功率/dBm	5
激光频率/THz	193.1
SSMF长度/km	100
SSMF衰减/(dB·km^-1)	0.2
SSMF色散/(ps·(nm·km)^-1)	16
SSMF色散斜率/(s·m^-3)	0.08×10³
SSMF群折射率	1.47
SSMF偏振模色散/ $(s \cdot m^{\frac{1}{2}})$	0.000 316
SSMF非线性系数/(m²·W^-1)	2.6×10^-20
光纤传输回路（loop）环数N	4
EDFA功率/dBm	20
光谱仪分辨率/nm	0.1

References 17

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真实值	没有攻击（预测值）	轻度带内干扰攻击（预测值）	强度带内干扰攻击（预测值）	轻度带外干扰攻击（预测值）	强度带外干扰攻击（预测值）
没有攻击	96.00%	4.00%	—	—	—
轻度带内干扰攻击	—	100.00%	—	—	—
强度带内干扰攻击	—	—	100.00%	—	—
轻度带外干扰攻击	—	—	—	100.00%	—
强度带外干扰攻击	—	—	—	—	100.00%

真实值	没有攻击（预测值）	轻度带内干扰攻击（预测值）	强度带内干扰攻击（预测值）	轻度带外干扰攻击（预测值）	强度带外干扰攻击（预测值）
没有攻击	100.00%	—	—	—	—
轻度带内干扰攻击	4.00%	96.00%	—	—	—
强度带内干扰攻击	—	1.00%	99.00%	—	—
轻度带外干扰攻击	—	—	—	100.00%	—
强度带外干扰攻击	—	—	—	—	100.00%

模型	训练时间/ms	测试时间/ms	平均准确率
BiLSTM	18 297	60	99.20%
1DCNN	52 298	86	99.00%

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

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Figures/Tables 19

References 17

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算法	平均训练时间/ms
ANN	12 522
DT	3 102
KNN	11
LDA	9 861
NB	186
RF	55
SVM	2 574

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