User influence in the propagation of malicious attacks

Jiang, J; Wen, S; Yu, S; Liu, B; Xiang, Y; Zhou, W

User influence in the propagation of malicious attacks

Jiang, J Wen, S Yu, S

Liu, B

Xiang, Y Zhou, W

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Publication Type:: Chapter
Citation:: Advances in Information Security, 2019, 73 pp. 21 - 39
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Jiang, J	en_US
dc.contributor.author	Wen, S	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617	en_US
dc.contributor.author	Xiang, Y	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.date.accessioned	2020-06-11T06:51:39Z
dc.date.accessioned	2020-06-11T06:51:40Z
dc.date.available	2020-06-11T06:51:39Z
dc.date.available	2020-06-11T06:51:40Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Advances in Information Security, 2019, 73 pp. 21 - 39	en_US
dc.identifier.uri	http://hdl.handle.net/10453/141363
dc.description.abstract	© Springer Nature Switzerland AG 2019. Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases and malicious rumors propagate within a society. Recently, researchers have found that unsolicited malicious attacks spread extremely fast through influential spreaders. For example, in April 23, 2013, the twitter account of Associated Press was hacked to spread the rumor that explosions at White House injured Obama. This led to both the DOW Jones industrial average and Standard & Poor’s 500 Index plunging about 1% before regaining their losses. Hence, identifying the most efficient ‘spreaders’ in a network becomes an important step towards restraining spread of malicious attacks. In this chapter, we investigate the methods of measuring influence of network nodes.	en_US
dc.relation.ispartof	Advances in Information Security	en_US
dc.relation.isbasedon	10.1007/978-3-030-02179-5_3	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	User influence in the propagation of malicious attacks	en_US
dc.type	Chapter
utslib.citation.volume	73	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
utslib.copyright.status	recently_added	*
pubs.publication-status	Published	en_US
pubs.volume	73	en_US

Abstract:

© Springer Nature Switzerland AG 2019. Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases and malicious rumors propagate within a society. Recently, researchers have found that unsolicited malicious attacks spread extremely fast through influential spreaders. For example, in April 23, 2013, the twitter account of Associated Press was hacked to spread the rumor that explosions at White House injured Obama. This led to both the DOW Jones industrial average and Standard & Poor’s 500 Index plunging about 1% before regaining their losses. Hence, identifying the most efficient ‘spreaders’ in a network becomes an important step towards restraining spread of malicious attacks. In this chapter, we investigate the methods of measuring influence of network nodes.

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http://hdl.handle.net/10453/141363