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AuthorTian, Zhihong
AuthorLuo, Chaochao
AuthorQiu, Jing
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-12-14T16:25:44Z
Publication Date2020-03-01
Publication NameIEEE Transactions on Industrial Informatics
Identifierhttp://dx.doi.org/10.1109/TII.2019.2938778
CitationTian, Z., Luo, C., Qiu, J., Du, X., & Guizani, M. (2019). A distributed deep learning system for web attack detection on edge devices. IEEE Transactions on Industrial Informatics, 16(3), 1963-1971.‏
ISSN15513203
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078480541&origin=inward
URIhttp://hdl.handle.net/10576/37266
AbstractWith 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.
SponsorThis research is supported in part by the National Key Research and Development Plan under Grant 2018YFB0803504 and Grant 2018YEB1004003, in part by the Guangdong Province Key Research and Development Plan under Grant 2019B010137004, and in part by the National Natural Science Foundation of China under Grant U1636215, Grant 61871140, Grant 61872100, and Grant 61572153. Paper no. TII-19-1526.
Languageen
PublisherIEEE Computer Society
SubjectDistributed deep dearning
distributed system
edge of things
web attack detection
TitleA Distributed Deep Learning System for Web Attack Detection on Edge Devices
TypeArticle
Pagination1963-1971
Issue Number3
Volume Number16
dc.accessType Abstract Only


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