TY - GEN
T1 - The Robustness of Machine Learning Models Using MLSecOps
T2 - 14th International Conference on Information and Communication Technology and System, ICTS 2023
AU - Saputra, Adi
AU - Suryani, Erma
AU - Rakhmawati, Nur Aini
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Forecasting delivery services is a key aspect of modern delivery service operations that significantly contributes to the optimization of operations and the enhancement of customer satisfaction. Machine learning can assist in predicting the delivery time. One method to enhance security in machine learning is the implementation of MLSecOps. MLSecOps, or Machine Learning Security Operations, streamlines the process of deploying, monitoring, and maintaining machine learning models to ensure consistent and reliable performance in production environments. Cybersecurity was also integrated to enhance the security, robustness, and resilience of these models. This study applies MLSecOps to forecast delivery services to enhance the robustness of machine learning models. The MLSecOps tool utilized is the Adversarial Robustness Toolbox (ART). The results of testing the machine learning model on Forecasting Delivery Services show robustness to attacks such as boundary and backdoor attacks.
AB - Forecasting delivery services is a key aspect of modern delivery service operations that significantly contributes to the optimization of operations and the enhancement of customer satisfaction. Machine learning can assist in predicting the delivery time. One method to enhance security in machine learning is the implementation of MLSecOps. MLSecOps, or Machine Learning Security Operations, streamlines the process of deploying, monitoring, and maintaining machine learning models to ensure consistent and reliable performance in production environments. Cybersecurity was also integrated to enhance the security, robustness, and resilience of these models. This study applies MLSecOps to forecast delivery services to enhance the robustness of machine learning models. The MLSecOps tool utilized is the Adversarial Robustness Toolbox (ART). The results of testing the machine learning model on Forecasting Delivery Services show robustness to attacks such as boundary and backdoor attacks.
KW - Adversarial Robustness Toolbox
KW - MLSecOps
KW - Random Forest
KW - backdoor attack
KW - boundary attack
KW - forecasting delivery service
UR - http://www.scopus.com/inward/record.url?scp=85180367096&partnerID=8YFLogxK
U2 - 10.1109/ICTS58770.2023.10330833
DO - 10.1109/ICTS58770.2023.10330833
M3 - Conference contribution
AN - SCOPUS:85180367096
T3 - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
SP - 265
EP - 270
BT - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 October 2023 through 5 October 2023
ER -