Heartfield, Ryan, Loukas, George ORCID: 0000-0003-3559-5182 and Gan, Diane ORCID: 0000-0002-0920-7572 (2017) An eye for deception: A case study in utilizing the human-as-a-security-sensor paradigm to detect zero-day semantic social engineering attacks. In: 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE. ISBN 978-1-5090-5757-3 (doi:https://doi.org/10.1109/SERA.2017.7965754)

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Abstract

In a number of information security scenarios, human beings can be better than technical security measures at detecting threats. This is particularly the case when a threat is based on deception of the user rather than exploitation of a specific technical flaw, as is the case of spear-phishing, application spoofing, multimedia masquerading and other semantic social engineering attacks. Here, we put the concept of the humanas-a-security-sensor to the test with a first case study on a small number of participants subjected to different attacks in a controlled laboratory environment and provided with a mechanism to report these attacks if they spot them. A key challenge is to estimate the reliability of each report, which we address with a machine learning approach. For comparison, we evaluate the ability of known technical security countermeasures in detecting the same threats. This initial proof of concept study shows that the concept is viable.

Item Type:	Conference Proceedings
Title of Proceedings:	2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA)
Additional Information:	Conference held from 7-9 June 2017, London, UK.
Uncontrolled Keywords:	Human-as-a-Sensor; Social engineering; Semantic attacks; Cyber security
Subjects:	Q Science > QA Mathematics
Faculty / Department / Research Group:	Faculty of Liberal Arts & Sciences Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC) Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Related URLs:	Organisation
Last Modified:	26 Nov 2020 22:34
Selected for GREAT 2016:	None
Selected for GREAT 2017:	None
Selected for GREAT 2018:	None
Selected for GREAT 2019:	None
Selected for REF2021:	None
URI:	http://gala.gre.ac.uk/id/eprint/16703

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