Detecting abnormal service performance is significant for Internet-based service management and operation. Recent advances in anomaly detection methods prefer unsupervised learning algorithms since they can work without manually labelled data. However, existing unsupervised methods converge into suboptimal solutions due to their heuristic-based objectives. Moreover, they frequently rely on the strong assumption that noise follows a Gaussian distribution, and their detection accuracy is also highly sensitive to threshold settings. To detect anomalies precisely and robustly, we present Adran , an unsupervised anomaly detection model that introduces adversarial learning into a reconstructive model, generating a reconstructive adversarial network with an anomaly detection-based training objective. It tolerates non-Gaussian noise by activating the discriminator with a non-smooth function. Our experimental results demonstrate that Adran achieves an improvement of $\geq 32\%$ over the state-of-the-art methods in terms of F-score . Moreover, the robustness analysis demonstrates that it is reasonably easy and straightforward to set an appropriate threshold using Adran .

中文翻译：

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使用重建对抗网络的鲁棒异常检测

检测异常的服务性能对于基于互联网的服务管理和运营具有重要意义。异常检测方法的最新进展更喜欢无监督学习算法，因为它们可以在没有手动标记数据的情况下工作。然而，现有的无监督方法由于其基于启发式的目标而收敛为次优解决方案。此外，他们经常依赖于噪声遵循高斯分布的强烈假设，并且他们的检测精度对阈值设置也高度敏感。为了精确而稳健地检测异常，我们提出阿德兰 ，一种无监督的异常检测模型，将对抗性学习引入重建模型，生成具有基于异常检测的训练目标的重建对抗网络。它通过激活具有非平滑函数的鉴别器来容忍非高斯噪声。我们的实验结果表明阿德兰 实现了 $\geq 32\%$ 在最先进的方法方面 F-score . 此外，稳健性分析表明，使用以下方法设置适当的阈值是相当容易和直接的阿德兰 .