Anomaly detection plays a crucial role in ensuring network robustness.
However, implementing intelligent alerting systems becomes a challenge when
considering scenarios in which anomalies can be caused by both malicious and
non-malicious events, leading to the difficulty of determining anomaly
patterns. The lack of labeled data in the computer networking domain further
exacerbates this issue, impeding the development of robust models capable of
handling real-world scenarios. To address this challenge, in this paper, we
propose an end-to-end anomaly detection model development pipeline. This
framework makes it possible to consume user feedback and enable continuous
user-centric model performance evaluation and optimization. We demonstrate the
efficacy of the framework by way of introducing and bench-marking a new
forecasting model -- named \emph{Lachesis} -- on a real-world networking
problem. Experiments have demonstrated the robustness and effectiveness of the
two proposed versions of \emph{Lachesis} compared with other models proposed in
the literature. Our findings underscore the potential for improving the
performance of data-driven products over their life cycles through a harmonized
integration of user feedback and iterative development.

我们提出了一个端到端的异常检测模型开发流程，通过消耗用户反馈并进行持续的用户中心模型性能评估和优化，以解决网络中恶意和非恶意事件引发异常的模式确定困难和标记数据缺失的问题。我们通过引入和基准测试一种名为 Lachesis 的新型预测模型，在现实世界的网络问题上展示了该框架的效力。实验结果表明，与文献中提出的其他模型相比，我们提出的两个版本的 Lachesis 具有鲁棒性和有效性。我们的研究强调了通过用户反馈和迭代式开发在数据驱动产品的整个生命周期中提高性能的潜力。