SEADer++: social engineering attack detection in online environments using machine learning-Reference-Cited by-同舟云学术

SEADer++: social engineering attack detection in online environments using machine learning

Published:2020-04-10 Issue:3 Volume:4 Page:346-362
ISSN:2475-1839
Container-title:Journal of Information and Telecommunication
language:en
Short-container-title:Journal of Information and Telecommunication

Author:

Lansley Merton¹,Mouton Francois²³^ORCID,Kapetanakis Stelios³,Polatidis Nikolaos⁴^ORCID

Affiliation:

1. Department of Informatics, University of Sussex, Brighton, UK

2. Department of Applied Science, Noroff University College, Oslo, Norway

3. Department of Computer Science, University of the Western Cape, Bellville, South Africa

4. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, UK

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/24751839.2020.1747001

Reference20 articles.

1. Bezuidenhout, M. Mouton, F. & Venter, H. S. (2010). Social engineering attack detection model: Seadm. In 2010 Information Security for South Africa (pp. 1–8).

2. Bhakta, R. & Harris, I. G. (2015). Semantic analysis of dialogs to detect social engineering attacks. In Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) (pp. 424–427).

3. Detecting semantic social engineering attacks with the weakest link: Implementation and empirical evaluation of a human-as-a-security-sensor framework

4. Hoeschele, M. & Rogers, M. (2005). Detecting social engineering. In Pollitt, M. and Shenoi, S. (Eds.), Advances in digital forensics (pp. 67–77). Springer US.

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