Anomalies are common in network system monitoring. When manifested as network threats to be mitigated, service outages to be prevented, and security risks to be ameliorated, detecting such anomalous network behaviors becomes of great importance. However, the growing scale and complexity of the mobile communication networks, as well as the ever-increasing amount and dimensionality of network surveillance data, make it extremely difficult to monitor a mobile network and discover abnormal network behaviors. Recent advances in machine learning allow obtaining near-optimal solutions to complicated decision making problems with many sources of uncertainty that cannot be accurately characterized by traditional mathematical models. However, most machine learning algorithms are centralized, which renders them inapplicable to large-scale distributed wireless networks with tens of millions of mobile devices. In this article, we present fog intelligence, a distributed machine learning architecture that enables intelligent wireless network management. It preserves the advantage of both edge processing and centralized cloud computing. Furthermore, the proposed architecture is scalable, privacy-preserving, and well suited for intelligent management of a distributed wireless network.

中文翻译：

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用于网络异常检测的雾智能


网络系统监控中常见异常现象。当表现为需要缓解网络威胁、防止服务中断、改善安全风险时，检测此类异常网络行为就变得非常重要。然而，随着移动通信网络规模和复杂性的不断增长，网络监控数据的数量和维度不断增加，移动网络监控和网络异常行为发现变得异常困难。机器学习的最新进展使得能够为复杂的决策问题获得近乎最优的解决方案，这些问题具有许多不确定性来源，而传统数学模型无法准确表征这些不确定性来源。然而，大多数机器学习算法都是集中式的，这使得它们不适用于拥有数千万移动设备的大规模分布式无线网络。在本文中，我们介绍了雾智能，这是一种分布式机器学习架构，可实现智能无线网络管理。它保留了边缘处理和集中式云计算的优势。此外，所提出的架构具有可扩展性、隐私保护性，并且非常适合分布式无线网络的智能管理。