UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Towards Detecting Compromised Accounts on Social Networks

Egele, M; Stringhini, G; Kruegel, C; Vigna, G; (2017) Towards Detecting Compromised Accounts on Social Networks. IEEE Transactions on Dependable and Secure Computing (TDSC) , 14 (4) pp. 447-460. 10.1109/TDSC.2015.2479616. Green open access

[thumbnail of thjp.pdf]
Preview
Text
thjp.pdf

Download (1MB) | Preview

Abstract

Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable -- they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.

Type: Article
Title: Towards Detecting Compromised Accounts on Social Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TDSC.2015.2479616
Publisher version: http://dx.doi.org/10.1109/TDSC.2015.2479616
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. © 2015 IEEE.
Keywords: Twitter, feature extraction, facebook, training, reliability, uniform resource locators
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1470859
Downloads since deposit
204Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item