Abstract

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.

Original languageEnglish
Title of host publication2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages265-270
Number of pages6
ISBN (Electronic)9798350312164
DOIs
Publication statusPublished - 2023
Event14th International Conference on Information and Communication Technology and System, ICTS 2023 - Surabaya, Indonesia
Duration: 4 Oct 20235 Oct 2023

Publication series

Name2023 14th International Conference on Information and Communication Technology and System, ICTS 2023

Conference

Conference14th International Conference on Information and Communication Technology and System, ICTS 2023
Country/TerritoryIndonesia
CitySurabaya
Period4/10/235/10/23

Keywords

  • Adversarial Robustness Toolbox
  • MLSecOps
  • Random Forest
  • backdoor attack
  • boundary attack
  • forecasting delivery service

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