TY - GEN
T1 - Self-Reporting System for ATM Incidents Detection Using Machine Learning Techniques
AU - Nkrumah, Ivy Payne
AU - Sowah, Robert Adjetey
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - ATM security incidents are a significant concern for financial institutions, leading to financial losses and service downtimes for users. A Self-Reporting System for ATM Incident Detection (SRSAID) is proposed to address these issues using tampering images and R-CNN fine-tuned by a regression model for automatic detection. Python 3 is employed on Raspberry Pi, utilizing GPS and GSM modules for periodic updates. Experiments reveal a 96% accuracy with ssdlite-mobilenet-V2, outperforming ALEXNET's 80%. A web-based interface supports decision-making, reducing downtime experiences.
AB - ATM security incidents are a significant concern for financial institutions, leading to financial losses and service downtimes for users. A Self-Reporting System for ATM Incident Detection (SRSAID) is proposed to address these issues using tampering images and R-CNN fine-tuned by a regression model for automatic detection. Python 3 is employed on Raspberry Pi, utilizing GPS and GSM modules for periodic updates. Experiments reveal a 96% accuracy with ssdlite-mobilenet-V2, outperforming ALEXNET's 80%. A web-based interface supports decision-making, reducing downtime experiences.
KW - ATM security incidents
KW - R-CNN
KW - ssdlite_mobilenet_V2
UR - http://www.scopus.com/inward/record.url?scp=85217863550&partnerID=8YFLogxK
U2 - 10.1109/ICAST61769.2024.10856485
DO - 10.1109/ICAST61769.2024.10856485
M3 - Conference contribution
AN - SCOPUS:85217863550
T3 - IEEE International Conference on Adaptive Science and Technology, ICAST
BT - Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024
PB - IEEE Computer Society
T2 - 9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024
Y2 - 24 October 2024 through 26 October 2024
ER -