Fog Intelligence for Network Anomaly Detection

TL;DR

This paper introduces fog intelligence, a distributed machine learning framework designed for scalable, privacy-preserving network anomaly detection in large-scale wireless networks, combining edge and cloud processing.

Contribution

It proposes a novel fog intelligence architecture that enhances network anomaly detection by integrating distributed machine learning with scalability and privacy features.

Findings

Scalable and privacy-preserving network management

Effective detection of network anomalies in large-scale wireless networks

Combines edge processing with centralized cloud computing

Abstract

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 the network surveillance data, make it extremely difficult to monitor a mobile network and discover abnormal network behaviors. Recent advances in machine learning allow for 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 a 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. In addition, the proposed architecture is scalable, privacy-preserving, and well suited for intelligent management of a distributed wireless network.