The advancement of networking platforms for assured online services requires robust and effective network intelligence systems against anomalous events and malicious threats. With the rapid development of modern communication technologies, artificial intelligence, and the revolution of computing devices, cloud computing empowered network intelligence will inevitably become a core platform for various smart applications. While cloud computing provides strong and powerful computation, storage, and networking services to detect and defend cyber threats, edge computing on the other hand will deliver more benefits in specific yet potential critical areas. In this paper, we present a study on the data-driven edge intelligence for robust network anomaly detection. We first highlight the main motivations for edge intelligence, and then propose an intelligence system empowered by edge computing for network anomaly detection. We further propose a scheme on the data-driven robust network anomaly detection. In the proposed scheme, four phases are designed to incorporate with data-driven approaches to train a learning model which is able to detect and identify a network anomaly in a robust way. In the performance evaluations with data experiments, we demonstrate that the proposed scheme achieves the robustness of trained model and the efficiency on the detection of specific anomalies.

中文翻译：

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用于稳健网络异常检测的数据驱动边缘智能

用于保证在线服务的网络平台的进步需要强大而有效的网络智能系统来抵御异常事件和恶意威胁。随着现代通信技术、人工智能和计算设备革命的快速发展，云计算赋能的网络智能必然成为各种智能应用的核心平台。虽然云计算提供强大的计算、存储和网络服务来检测和防御网络威胁，但另一方面，边缘计算将在特定但潜在的关键领域带来更多好处。在本文中，我们提出了一项关于用于稳健网络异常检测的数据驱动边缘智能的研究。我们首先强调边缘智能的主要动机，然后提出一种边缘计算赋能的智能系统，用于网络异常检测。我们进一步提出了一种数据驱动的鲁棒网络异常检测方案。在所提出的方案中，设计了四个阶段以结合数据驱动方法来训练学习模型，该模型能够以稳健的方式检测和识别网络异常。在数据实验的性能评估中，我们证明了所提出的方案实现了训练模型的鲁棒性和特定异常检测的效率。四个阶段旨在结合数据驱动的方法来训练学习模型，该模型能够以稳健的方式检测和识别网络异常。在数据实验的性能评估中，我们证明了所提出的方案实现了训练模型的鲁棒性和特定异常检测的效率。四个阶段旨在结合数据驱动的方法来训练学习模型，该模型能够以稳健的方式检测和识别网络异常。在数据实验的性能评估中，我们证明了所提出的方案实现了训练模型的鲁棒性和特定异常检测的效率。