College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710699, China.
Comput Intell Neurosci. 2022 Oct 26;2022:8753323. doi: 10.1155/2022/8753323. eCollection 2022.
In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. However, there are problems of unstable generator training, missed detection of anomalous data, and inconsistency between the discriminator's discriminant and the anomaly detection target in GAN networks. Aiming at the above problems, the paper proposes a DUAL-ADGAN (Dual Anomaly Detection Generative Adversarial Networks) model for the detection of anomalous data in time series. First, the Wasserstein distance satisfying the Lipschitz constraint is used as the loss function of the data reconstruction module, which improves the stability of the traditional GAN network training. Second, by adding a data prediction module to the DUAL-ADGAN model, the distinction between abnormal and normal samples is increased, and the rate of missing abnormal data in the model is reduced. Third, by introducing the Fence-GAN loss function, the discriminator is aligned with the anomaly detection target, which effectively reduces the anomaly data false detection rate of the DUAL-ADGAN model. Finally, anomaly scores derived from the DUAL-ADGAN model are compared with dynamic thresholds to detect anomalies. The experimental results show that the average F1 of the DUAL-ADGAN model is 0.881, which is better than the other nine baseline models. The conclusions demonstrate that the DUAL-ADGAN model proposed in the paper is more stable in training while effectively solving the problems of anomaly miss detection and discriminator inconsistency with the anomaly detection target in the anomaly detection task.
近年来，时间序列数据中的异常检测技术在制造业、网络安全等领域得到了广泛应用。同时，基于生成对抗网络（GAN）的各种异常检测模型逐渐应用于时间序列异常检测任务中。然而，GAN 网络中存在生成器训练不稳定、异常数据漏检、判别器判别与异常检测目标不一致等问题。针对上述问题，本文提出了一种用于时间序列异常数据检测的 DUAL-ADGAN（双异常检测生成对抗网络）模型。首先，使用满足 Lipschitz 约束的 Wasserstein 距离作为数据重构模块的损失函数，提高了传统 GAN 网络训练的稳定性。其次，通过在 DUAL-ADGAN 模型中添加数据预测模块，增加了异常和正常样本之间的区别，减少了模型中异常数据的漏检率。第三，通过引入 Fence-GAN 损失函数，使判别器与异常检测目标保持一致，有效降低了 DUAL-ADGAN 模型的异常数据误检率。最后，通过比较 DUAL-ADGAN 模型导出的异常得分与动态阈值来检测异常。实验结果表明，DUAL-ADGAN 模型的平均 F1 值为 0.881，优于其他九个基线模型。结论表明，本文提出的 DUAL-ADGAN 模型在训练过程中更加稳定，同时有效解决了异常检测任务中异常漏检和判别器与异常检测目标不一致的问题。
