基于Transformer-VAE的ADS-B异常检测方法

doi:10.12305/j.issn.1001-506X.2023.11.37

Abstract

Abstract:

Aiming at the problem that the existing anomaly detection algorithm cannot simultaneously capture the long-range dependency, randomness and overall characteristics in the automatic dependent surveillance-broadcast (ADS-B) message sequence, an anomaly detection algorithm for ADS-B based on variational autoencoder (VAE) and Transformer is proposed. The Transformer encoder and decoder are used as the inference network and generation network of VAE to learn the timing distribution of ADS-B packet sequence. The long-range dependence relationship in ADS-B packet sequence is modeled by the self-attention mechanism of Transformer encoder, and random variables reflecting the global information of ADS-B packet sequence are generated. A special fusion module is used to fuse the global random variables with the output of Transformer decoder to realize the reconstruction of ADS-B packets. Finally, anomaly is detected through reconstruction error. Experiments show that the method outperforms the relevant baseline algorithms under different attack scenarios.

Key words: automatic dependent surveillance-broadcast (ADS-B), anomaly detection, variational autoencoder, deep learning

CLC Number:

V221+.3

Jianli DING, Qiqi ZHANG, Jing WANG, Weigang HUO. ADS-B anomaly detection method based on Transformer-VAE[J]. Systems Engineering and Electronics, 2023, 45(11): 3680-3689.

Figures/Tables 11

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References 24

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模型	评估指标	随机噪声	幽灵飞机	航向偏移	速度偏移	DOS攻击
OC-SVM	精确率	79.23	55.19	76.20	80.46	00.00
	召回率	67.63	9.80	26.53	61.21	00.00
	F1分数	73.01	16.64	40.11	69.52	-
IF	精确率	66.40	48.97	26.72	27.56	00.00
	召回率	82.60	21.68	51.60	57.49	00.00
	F1分数	73.61	30.05	35.20	37.25	-
LOF	精确率	30.16	34.37	31.16	26.67	00.00
	召回率	87.25	92.55	91.03	91.32	00.00
	F1分数	44.74	50.13	46.43	41.28	-
LSTM-AE	精确率	92.01	77.75	79.67	83.81	82.58
	召回率	84.40	89.61	86.48	76.84	90.14
	F1分数	88.04	83.25	82.94	80.17	84.80
LSTM-VAE	精确率	92.99	79.32	80.76	86.50	87.92
	召回率	87.58	88.79	89.03	78.21	91.31
	F1分数	90.20	83.78	84.69	82.14	89.58
Transformer-VAE	精确率	98.78	91.02	94.91	91.77	91.45
	召回率	91.42	93.83	98.32	90.61	93.76
	F1分数	94.96	92.40	96.58	91.19	92.60

模型	精确率	召回率	F1分数
(1)	90.64	87.30	88.94
(2)	91.12	90.89	91.00
(3)	93.17	92.42	92.79

模型	评估指标	k
模型	评估指标	100	200	300	400	500
LSTM-VAE	精确率	88.64	88.89	93.05	90.23	90.90
	召回率	83.70	95.75	86.30	87.70	84.73
	F1分数	86.10	92.19	89.55	88.94	87.71
Transformer-VAE	精确率	96.40	92.93	95.94	92.97	93.45
	召回率	89.93	95.88	94.93	94.91	92.27
	F1分数	93.05	94.38	95.43	93.93	92.85

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ADS-B anomaly detection method based on Transformer-VAE

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