基于Deep SVDD的通信信号异常检测方法

doi:10.12305/j.issn.1001-506X.2022.07.29

Abstract

Abstract:

To solve the problem of anomaly detection (AD) of the non-ideal channel in complex electronic countermeasures, a deep learning based method is presented. First, the feasibility of using time-frequency in-phase/quadrature (I/Q) sampling data for anomaly detection (AD) is analyzed. Then, a deep learning network architecture is designed and an AD method based on deep support vector data description (Deep SVDD) and modulation classification is proposed. Simulation and experimental results show that the detection performance and real-time performance of the method are significantly improved compared with the classical algorithms of one-class classification, and the performance is robust in non-ideal channel environment. The method is validated on a sample machine and is of high application value.

Key words: anomaly detection (AD), deep support vector data description (Deep SVDD), modulation classification, interference warning

CLC Number:

TN911.7

Ying KANG, Zhihua ZHAO, Hao WU, Yaxing LI, Jin MENG. Deep SVDD-based anomaly detection method for communication signals[J]. Systems Engineering and Electronics, 2022, 44(7): 2319-2328.

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References 34

1	吴灏, 周亮, 李亚星, 等. 基于卷积神经网络和稀疏滤波的调制识别方法[J]. 系统工程与电子技术, 2019, 41 (9): 2114- 2121.
	WU H , ZHOU L , LI Y S . Modulation classification based on convolutional neural network and sparse filtering[J]. Systems Engineering and Electronics, 2019, 41 (9): 2114- 2121.
2	刘义, 赵晶, 刘佳楠, 等. 基于作战效能的战场电磁环境分级描述方法[J]. 系统工程与电子技术, 2011, 33 (5): 1059- 1062. doi: 10.3969/j.issn.1001-506X.2011.05.20
	LIU Y , ZHAO J , LIU J N , et al. Novel battlefield electromagnetic environment classification method based on combat efficacy of weapon systems[J]. Systems Engineering and Electronics, 2011, 33 (5): 1059- 1062. doi: 10.3969/j.issn.1001-506X.2011.05.20
3	CHANDOLA V , BANERJEE A , KUMAR V , et al. Anomaly detection: a survey[J]. ACM Computing Surveys, 2009, 41 (3): 1- 58.
4	WANG B , LIU K R . Advances in cognitive radio networks: a survey[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 5 (1): 5- 23.
5	TANDRA R , SAHAI A . SNR walls for signal detection[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2 (1): 4- 17. doi: 10.1109/JSTSP.2007.914879
6	TURIN G L . An introduction to matched filters[J]. IRE Trans.on Information Theory, 1960, 6 (3): 311- 329. doi: 10.1109/TIT.1960.1057571
7	YE Z, GROSSPIETSCH J, MEMIK G. Spectrum sensing using cyclostationary spectrum density for cognitive radios[C]//Proc. of the IEEE Workshop on Signal Processing Systems, 2007.
8	LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444. doi: 10.1038/nature14539
9	HINTON G E , OSINDERO S , TEH Y W . A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, (521): 436- 444.
10	MINAEE S , KALCHBRENNER N , CAMBRIA E , et al. Deep learning-based text classification: a comprehensive review[J]. ACM Computing Surveys, 2021, 54 (3): 1- 40.
11	MINAEE S, BOYKOV Y Y, PORIKLI F, et al. Image segmentation using deep learning: a survey[EB/OL]. [2022-05-25]. https://arxiv.org/abs/2001.05566.
12	O'SHEA T J, CLANCY T C, MCGWIER R W. Recurrent neural radio anomaly detection[EB/OL]. [2022-05-25]. https://arxiv.org/abs/1611.00301.
13	PELLACO L, SINGH N, JALDEN J. Spectrum prediction and interference detection for satellite communications[C]//Proc. of the 37th International Communications Satellite Systems Conference, 2019.
14	CONN M A, JOSYULA D. Radio frequency classification and anomaly detection using convolutional neural networks[C]//Proc. of the IEEE Radar Conference, 2019.
15	LI Z J, XIAO Z J, WANG B L, et al. Scaling deep learning models for spectrum anomaly detection[C]//Proc. of the 20th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2019: 291-300.
16	ZHOU X H , XIONG J , ZHANG X C , et al. A radio anomaly detection algorithm based on modified generative adversarial network[J]. IEEE Wireless Communications Letters, 2021, 10 (7): 1552- 1556. doi: 10.1109/LWC.2021.3074135
17	ZHANG L, HUANG C X, TANG H, et al. DTV radio spectrum anomaly detection based on an improved GAN[C]//Proc. of the 33rd General Assembly and Scientific Symposium of the International Union of Radio Science, 2020.
18	YOKKAMPON U, CHUMKAMON S, MOWSHOWITZ A, et al. Anomaly detection using variational autoencoder with spectrum analysis for time series data[C]//Proc. of the 9th International Conference on Informatics, Electronics & Vision, 2020.
19	TOMA A, KRAYANI A, MARCENARO L, et al. Deep learning for spectrum anomaly detection in cognitive mmWave radios[C]//Proc. of the IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 2020.
20	GHANNEY Y, AJIB W. Radio frequency interference detection using deep learning[C]//Proc. of the IEEE 91st Vehicular Technology Conference, 2020.
21	XU J, TIAN Y, YUAN S, et al. Noise attention based spectrum anomaly detection method for unauthorized bands[EB/OL]. [2022-05-25]. https://arxiv.org/abs/2104.08517.
22	RAJENDRAN S , MEERT W , LENDERS V , et al. Unsupervised wireless spectrum anomaly detection with interpretable features[J]. IEEE Trans.on Cognitive Communications Networking, 2019, 5 (3): 637- 647. doi: 10.1109/TCCN.2019.2911524
23	RAJENDRAN S , LENDERS V , MEERT W , et al. Crowdsourced wireless spectrum anomaly detection[J]. IEEE Trans.on Cognitive Communications and Networking, 2019, 6 (2): 694- 703.
24	TOMA A , KRAYANI A , FARRUKH M , et al. AI-based abnormality detection at the PHY-layer of cognitive radio by learning generative models[J]. IEEE Trans.on Cognitive Communications and Networking, 2020, 6 (1): 21- 34. doi: 10.1109/TCCN.2020.2970693
25	SCHEIRER W J , ROCHA A D R , SAPKOTA A , et al. Toward open set recognition[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2013, 35 (7): 1757- 1772. doi: 10.1109/TPAMI.2012.256
26	WANG Y , LIU M , YANG J , et al. Data-driven deep learning for automatic modulation recognition in cognitive radios[J]. IEEE Trans.on Vehicular Technology, 2019, 68 (4): 4074- 4077. doi: 10.1109/TVT.2019.2900460
27	RUFF L, VANDERMEULEN R, GOERNITZ N, et al. Deep one-class classification[C]//Proc. of the International Conference on Machine Learning, 2018: 4393-4402.
28	周鑫, 何晓新, 郑昌文. 基于图像深度学习的无线电信号识别[J]. 通信学报, 2019, 40 (7): 114- 125.
	ZHOU X , HE X X , ZHENG C W . Radio signal recognition based on image deep learning[J]. Journal on Communications, 2019, 40 (7): 114- 125.
29	O'SHEA T J, CORGAN J, CLANCY T C. Convolutional radio modulation recognition networks[C]//Proc. of the International Conference on Engineering Applications of Neural Networks, 2016: 213-226.
30	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
31	Analog Devices. AD9361[EB/OL]. [2022-05-25]. www.analog.com/en/products/ad9361.html.
32	KINGMA D P, BA J. ADAM: a method for stochastic optimization[EB/OL]. [2022-05-25]. https://arxiv.org/abs/1412.6980.
33	CHALAPATHY R, MENON A K, CHAWLA S. Anomaly detection using one-class neural networks[EB/OL]. [2022-05-25]. https://arxiv.org/abs/1802.06360.
34	LIU F T, TING K M, ZHOU Z H. Isolation forest[C]//Proc. of the IEEE 8th International Conference on Data Mining, 2008: 413-422.

参数	数值
频率偏移/kHz	-20~20
相位噪声功率/ (dBc/Hz)	-84(频偏为1 kHz) -100(频偏为10 kHz) -105(频偏为100 kHz) -109(频偏为1 MHz)
白噪声/dB	0~30
降采样倍数	70
低通滤波截至频率/kHz	17.5
低通滤波过渡带宽/kHz	150

编号	输入	一维卷积核长度N_C	卷积核个数	残差单元个数	输出
1	4×1 024	1×35	16	1	1×256
2	4×1 024	1×35	16	2	1×256
3	4×1 024	1×35	32	1	1×256
4	4×1 024	1×35	32	2	1×256

编号	AUC	需优化参数(k个)
1	0.978 5	16 795 152
2	0.952 7	16 813 328
3	0.986 3	33 626 144
4	0.997 9	33 698 848

方法	数据样式	输入维度	网络深度(层)
AE-OCSVM	时频谱图	32×32×3	7
AE-IFOREST	时频谱图	32×32×3	7
本文方法	基带IQ	4×1 024	7

方法	预处理	网络推断时间	线型判决时间	总耗时
AE-OCSVM	118	3.6	0.3	121.9
AE-IFOREST	118	3.6	37	155.6
本文方法	0.3	19.2	—	19.5

Deep SVDD-based anomaly detection method for communication signals

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参数	数值
信道类型	瑞利衰落, 莱斯衰落
频率偏移/kHz	-1~1
发射功率/W	1
ISR/dB	-20~20

[1]	WU Hao, ZHOU Liang, LI Yaxing, GUO Yu, MENG Jin. Modulation classification based on convolutional neural network and sparse filtering [J]. Systems Engineering and Electronics, 2019, 41(9): 2114-2121.
[2]	YUAN Lifen, NING Shuguang, HE Yigang, LYU Mi, LU Jian. Modulation recognition method based on high-order cumulant feature learning [J]. Systems Engineering and Electronics, 2019, 41(9): 2122-2131.
[3]	JIN Xiao-yan， ZHOU Xi-yuan. Fast algorithm for maximum likelihood modulation classification [J]. Journal of Systems Engineering and Electronics, 2013, 35(3): 615-618.
[4]	ZHOU Xin， WU Ying. Signal modulation recognition based on KPCA and LDA [J]. Journal of Systems Engineering and Electronics, 2011, 33(7): 1611-1616.
[5]	CHENG Han-wen1， CHEN Liang2， WU Le-nan3. Weighted voting based on decision fusion for modulation  classification of multisystems [J]. Journal of Systems Engineering and Electronics, 2010, 32(2): 342-345.