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Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models
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作者 Virgile Simé Nyassi Franklin Tchakounté +3 位作者 blaise omer yenké Duplex Elvis Houpa Danga Magnuss Dufe Ngoran Jean Louis Kedieng Ebongue Fendji 《Journal of Intelligent Learning Systems and Applications》 2024年第3期274-315,共42页
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 ... 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. 展开更多
关键词 Social Engineering Hidden Markov Model Vishing Voice Mining
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