ADS-B abnormal data detection model based on WGAN-XGBoost

doi:10.16265/j.cnki.issn1003-3033.2025.08.1566

Abstract

Abstract:

To enhance aviation operation safety, improve airspace management efficiency, and enhance the defense capabilities of system against spoofing and interference, an anomaly data detection model was proposed based on WGAN-XGBoost. Firstly, WGAN was utilized to learn the intrinsic distribution of the preprocessed ADS-B data, generating abnormal data for augmenting and balancing the training dataset. Then, XGBoost algorithm was employed to train the mixed dataset, building the final abnormal classification detector. Finally, the performance comparisons were conducted through experiments with benchmark models such as Naive Bayes, Logistic Regression, and Perceptron. The results show that the performance of XGBoost is superior to that of all comparison models including accuracy, precision, recall, and F₁ score, with accuracy and precision both exceeding 0.999. The total detection time for 243 792 data points is 2.070 2 s, with an average detection time of 0.008 5 ms per data point. It achieves the optimal balance between detection performance and time cost and has been validated by real abnormal events, demonstrating good practicality and applicability.

Key words: Wasserstein generative adversarial network (WGAN), extreme gradient boosting (XGBoost), automatic dependent surveillance-broadcast (ADS-B), abnormal data detection, naive Bayesian

CLC Number:

X924.2安全监测技术与设备

LI Huaiqian, CHEN Yuhao, FU Yuxiang, SHEN Jiayi. ADS-B abnormal data detection model based on WGAN-XGBoost[J]. China Safety Science Journal, 2025, 35(8): 188-195.

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评价指标	XGBoost	朴素贝叶斯	逻辑回归	感知机
准确率	0.999 4	0.947 2	0.824 8	0.814 2
精确率	1.000 0	0.824 5	0.451 5	0.214 2
召回率	0.996 2	0.868 9	0.229 1	0.042 4
F₁分数	0.998 1	0.846 1	0.303 9	0.070 8
ROC-AUC	1.000 0	0.986 4	0.865 9	0.781 1
PR-AUC	0.999 9	0.942 9	0.439 6	0.325 4

模型	总检测时间/s	平均每次检测时间/ms	异常检测次数
XGBoost	2.070 2	0.008 5	2437 92
朴素贝叶斯	0.157 7	0.000 6	2437 92
逻辑回归	0.757 7	0.003 1	2437 92
感知机	0.547 8	0.002 2	2437 92