A Distributed Deep Learning System for Web Attack Detection on Edge Devices

Abstract

With the development of Internet of Things (IoT) and cloud technologies, numerous IoT devices and sensors transmit huge amounts of data to cloud data centers for further processing. While providing us considerable convenience, cloud-based computing and storage also bring us many security problems, such as the abuse of information collection and concentrated web servers in the cloud. Traditional intrusion detection systems and web application firewalls are becoming incompatible with the new network environment, and related systems with machine learning or deep learning are emerging. However, cloud-IoT systems increase attacks against web servers, since data centralization carries a more attractive reward. In this article, based on distributed deep learning, we propose a web attack detection system that takes advantage of analyzing URLs. The system is designed to detect web attacks and is deployed on edge devices. The cloud handles the above challenges in the paradigm of the Edge of Things. Multiple concurrent deep models are used to enhance the stability of the system and the convenience in updating. We implemented experiments on the system with two concurrent deep models and compared the system with existing systems by using several datasets. The experimental results with 99.410% in accuracy, 98.91% in true positive rate (TPR), and 99.55% in detection rate of normal requests (DRN) demonstrate the system is competitive in detecting web attacks.