A Novel HTTP Anomaly Detection Framework Based on Edge Intelligence for the Internet of Things (IoT)

Abstract

In recent years, with the rapid development of the Internet of Things (IoT), various applications based on IoT have become more and more popular in industrial and living sectors. However, Hypertext Transfer Protocol (HTTP) as a popular application protocol used in various IoT applications faces a variety of security vulnerabilities. This article proposes a novel HTTP anomaly detection framework based on edge intelligence for IoT. In this framework, both clustering and classification algorithms are used to quickly and accurately detect anomalies in the HTTP traffic for IoT. Unlike the existing works relying on a centralized server to perform anomaly detection, with the recent advances in edge intelligence, the proposed framework distributes the entire detection process to different nodes. A data processing method is proposed to divide the detection fields of HTTP data, which can eliminate redundant data and extract features from the fields of a HTTP header. Simulation results show that the proposed framework can significantly improve the speed and accuracy of HTTP anomaly detection, especially for unknown anomalies.