Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain

Chaudhary, P; Gupta, BB; Chang, X; Nedjah, N; Chui, KT

Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain

Chaudhary, P Gupta, BB Chang, X

Nedjah, N Chui, KT

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Technological Forecasting and Social Change, 2021, 168
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Chaudhary, P
dc.contributor.author	Gupta, BB
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Nedjah, N
dc.contributor.author	Chui, KT
dc.date.accessioned	2022-06-14T05:27:29Z
dc.date.available	2022-06-14T05:27:29Z
dc.date.issued	2021-07-01
dc.identifier.citation	Technological Forecasting and Social Change, 2021, 168
dc.identifier.issn	0040-1625
dc.identifier.issn	1873-5509
dc.identifier.uri	http://hdl.handle.net/10453/158165
dc.description.abstract	Advancement in embedded Nano-technologies empowers IoT technology which serves as the backbone technology for many small and medium enterprises. Evolution of smart devices proved as a supreme source of data, denoted as Big data, that is analyzed to instantly extricate constructive and valuable information that helps the organization in many ways. As it may embrace sensitive information, hence, it turned out to be the fascinating target of multitude of attacks that aims at stealing the information, resulting into privacy breaches. XSS attack is one such security attack that supports the attacker to intrude into user's personal space. Therefore, this paper is focused on designing an approach that detects XSS attack in IoT network to protect the data privacy. It employs Convolution Neural Network (CNN) to detect the XSS attack payload after applying certain data preparation methods. This approach helps in preventing privacy breaches which eventually helps the enterprises in strengthening their bond with their users. The experimental results unveiled that the approach achieves a detection accuracy of 99% after the successful execution of data preparation methods.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation.ispartof	Technological Forecasting and Social Change
dc.relation.isbasedon	10.1016/j.techfore.2021.120754
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	10 Technology, 14 Economics, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Science Studies
dc.title	Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain
dc.type	Journal Article
utslib.citation.volume	168
utslib.for	10 Technology
utslib.for	14 Economics
utslib.for	15 Commerce, Management, Tourism and Services
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-14T05:27:28Z
pubs.publication-status	Published
pubs.volume	168

Abstract:

Advancement in embedded Nano-technologies empowers IoT technology which serves as the backbone technology for many small and medium enterprises. Evolution of smart devices proved as a supreme source of data, denoted as Big data, that is analyzed to instantly extricate constructive and valuable information that helps the organization in many ways. As it may embrace sensitive information, hence, it turned out to be the fascinating target of multitude of attacks that aims at stealing the information, resulting into privacy breaches. XSS attack is one such security attack that supports the attacker to intrude into user's personal space. Therefore, this paper is focused on designing an approach that detects XSS attack in IoT network to protect the data privacy. It employs Convolution Neural Network (CNN) to detect the XSS attack payload after applying certain data preparation methods. This approach helps in preventing privacy breaches which eventually helps the enterprises in strengthening their bond with their users. The experimental results unveiled that the approach achieves a detection accuracy of 99% after the successful execution of data preparation methods.

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http://hdl.handle.net/10453/158165