TY - GEN
T1 - Machine learning detection for SMiShing frauds
AU - Boukari, Badr Eddine
AU - Ravi, Akshaya
AU - Msahli, Mounira
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/9
Y1 - 2021/1/9
N2 - Vishing and SMiShing frauds not only contribute to financial loss but also have social and psychological impact on the victims. The need for a simple implementable solution that protects the victims has been the necessity of the telecommunication industry for a long time. This demo displays the results of machine learning based detection system to detect SMiShing frauds and alert the users. This approach can be further adapted to detect phishing and vishing frauds.
AB - Vishing and SMiShing frauds not only contribute to financial loss but also have social and psychological impact on the victims. The need for a simple implementable solution that protects the victims has been the necessity of the telecommunication industry for a long time. This demo displays the results of machine learning based detection system to detect SMiShing frauds and alert the users. This approach can be further adapted to detect phishing and vishing frauds.
U2 - 10.1109/CCNC49032.2021.9369640
DO - 10.1109/CCNC49032.2021.9369640
M3 - Conference contribution
AN - SCOPUS:85102982256
T3 - 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
BT - 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
Y2 - 9 January 2021 through 13 January 2021
ER -