The prompt expansion of the Internet of Things (IoT) and its wide application in smart homes and transportation has brought tremendous convenience to people’s lives. However, the increase of IoT devices has also brought huge security problems, threatening people’s information and property security. This paper designs a new anomaly detection architecture based on the concept of the “Internet of intelligence”. It is a general architecture that can be applied to different IoT anomaly detection methods. The architecture effectively combines the blockchain and the IoT anomaly detection method, which can overcome the problems of data resource sharing and collective learning. At the same time, we propose a novel method for detecting abnormal HTTP traffic in IoT. It combines clustering and Autoencoder method to efficiently and exactly detect abnormal HTTP traffic in IoT devices. In addition, we propose an optimized feature extraction method, which is favorable to enhance the detection effect. Simulation results show the proposed architecture and method can enhance the detection performance of abnormal HTTP traffic in IoT and address the challenges of existing approaches.

中文翻译：

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基于智能互联网的HTTP异常检测架构


物联网的迅速发展及其在智能家居、智能交通等领域的广泛应用，给人们的生活带来了巨大的便利。然而，物联网设备的增加也带来了巨大的安全问题，威胁着人们的信息和财产安全。本文基于“智能互联网”的概念，设计了一种新的异常检测架构。它是一种通用架构，可应用于不同的物联网异常检测方法。该架构有效结合了区块链和物联网异常检测方法，可以克服数据资源共享和集体学习的问题。同时，我们提出了一种检测物联网中异常 HTTP 流量的新方法。它结合了聚类和自动编码器方法，可以高效、准确地检测物联网设备中的异常 HTTP 流量。此外，我们提出了一种优化的特征提取方法，有利于增强检测效果。仿真结果表明，所提出的架构和方法可以增强物联网中异常HTTP流量的检测性能，并解决现有方法的挑战。