MMWD: An efficient mobile malicious webpage detection framework based on deep learning and edge cloud. (19th January 2021)

• Record Type: Journal Article
• Authors: Liu, Yizhi; Zhu, Chaoqun; Wu, Yadi; Xu, Heng; Song, Jun
• Other Names: Oh Sangyoon guestEditor.; de Camargo Raphael Y. guestEditor.; Marozzo Fabrizio guestEditor.; Martins Wellington guestEditor.; Kołodziej Joanna guestEditor.; Jaatun Martin Gilje guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 18(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-19
• Subjects: deep learning -- edge computing -- machine learning -- social network -- web page detection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6191
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: In recent years, with the rapid development of mobile social networks and services, the research of mobile malicious webpage detection has become a hot topic. Most of the existing malicious webpage detection systems are deployed on desktop systems and servers. Due to the limitation of network transmission delay and computing resources, these existing solutions fail to provide the real‐time and lightweight properties for mobile webpage detection. In this paper, we propose an advanced mobile malicious webpage detection framework based on deep learning and edge cloud. Inspired by the idea of edge computing, a multidevice load optimization approach is first introduced to improve detection efficiency. Second, an automatic extraction approach based on deep learning model features is presented to enhance detection accuracy. Furthermore, detection systems can be flexibly deployed on edge nodes and servers, thus providing the properties of resource optimization deployment and real‐time detection. Finally, comparative analysis and performance evaluation are presented to show the detection efficiency and accuracy of the proposed framework.