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Lansley, M., Mouton, F., Kapetanakis, S. and Polatidis, N. (2020) SEADer++: Social Engineering Attack Detection in Online Environments Using Machine Learning. Journal of Information and Telecommunication, 4, 346-362.
https://doi.org/10.1080/24751839.2020.1747001

has been cited by the following article:

• TITLE: Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

  AUTHORS: Virgile Simé Nyassi, Franklin Tchakounté, Blaise Omer Yenké, Duplex Elvis Houpa Danga, Magnuss Dufe Ngoran, Jean Louis Kedieng Ebongue Fendji

  KEYWORDS: Social Engineering, Hidden Markov Model, Vishing, Voice Mining

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.16 No.3, August 30, 2024

  ABSTRACT: This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.