%0 Journal Article
%T Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models
%A Virgile Sim&#233
%A Nyassi
%A Franklin Tchakount&#233
%A Blaise Omer Yenk&#233
%A Duplex Elvis Houpa Danga
%A Magnuss Dufe Ngoran
%A Jean Louis Kedieng Ebongue Fendji
%J Journal of  Intelligent Learning Systems and Applications
%P 274-315
%@ 2150-8410
%D 2024
%I Scientific Research Publishing
%R 10.4236/jilsa.2024.163015
%X 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.
%K Social Engineering
%K Hidden Markov Model
%K Vishing
%K Voice Mining
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=135703