Cyberattack detection in mobile cloud computing: A deep learning approach

Nguyen, KK; Hoang, DT; Niyato, D; Wang, P; Nguyen, D; Dutkiewicz, E

Cyberattack detection in mobile cloud computing: A deep learning approach

Nguyen, KK Hoang, DT

Niyato, D Wang, P Nguyen, D

Dutkiewicz, E

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE Wireless Communications and Networking Conference, WCNC, 2018, 2018-April pp. 1 - 6
Issue Date:: 2018-06-08

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (1.41 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Nguyen, KK	en_US
dc.contributor.author	Hoang, DT https://orcid.org/0000-0002-9528-0863	en_US
dc.contributor.author	Niyato, D	en_US
dc.contributor.author	Wang, P	en_US
dc.contributor.author	Nguyen, D https://orcid.org/0000-0003-2659-8648	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.date.available	2020-05-25T19:21:34Z
dc.date.issued	2018-06-08	en_US
dc.identifier.citation	IEEE Wireless Communications and Networking Conference, WCNC, 2018, 2018-April pp. 1 - 6	en_US
dc.identifier.isbn	9781538617342	en_US
dc.identifier.issn	1525-3511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131928
dc.description.abstract	© 2018 IEEE. With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.	en_US
dc.relation.ispartof	IEEE Wireless Communications and Networking Conference, WCNC	en_US
dc.relation.isbasedon	10.1109/WCNC.2018.8376973	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Cyberattack detection in mobile cloud computing: A deep learning approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-April	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-April	en_US

Abstract:

© 2018 IEEE. With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/131928