A Novel Web Attack Detection System for Internet of Things via Ensemble Classification

Luo, Chaochao; Tan, Zhiyuan; Min, Geyong; Gan, Jie; Shi, Wei; Tian, Zhihong

Abstract

Internet of things (IoT) has become one of the fastestgrowing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this paper, we propose a novel Ensemble Deep Learning based Web Attack Detection System (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a realword dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates.

• Journal Article Type: Article
• Acceptance Date: Nov 13, 2020
• Online Publication Date: Nov 17, 2020
• Publication Date: 2021-08
• Deposit Date: Nov 15, 2020
• Publicly Available Date: Nov 16, 2020
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Electronic ISSN: 1941-0050
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 8
• Pages: 5810-5818
• DOI: https://doi.org/10.1109/tii.2020.3038761
• Keywords: IOT, Deep Learning, Ensemble Classifier, Web Attack Detection
• Public URL: http://researchrepository.napier.ac.uk/Output/2701436