MLOps-Enabled Security Strategies for Next-Generation Operational Technologies

Paper


Ahmad, Tazeem, Adnan, Mohd, Rafi, Saima, Akbar, Muhammad Azeem and Anwar, Ayesha. 2024. "MLOps-Enabled Security Strategies for Next-Generation Operational Technologies." 28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24). Salerno, Italy 18 - 21 Jun 2024 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3661167.3661283
Paper/Presentation Title

MLOps-Enabled Security Strategies for Next-Generation Operational Technologies

Presentation TypePaper
AuthorsAhmad, Tazeem, Adnan, Mohd, Rafi, Saima, Akbar, Muhammad Azeem and Anwar, Ayesha
Journal or Proceedings TitleProceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24)
Journal Citationpp. 662-667
Number of Pages6
Year2024
PublisherAssociation for Computing Machinery (ACM)
Place of PublicationUnited States
ISBN9798400717017
Digital Object Identifier (DOI)https://doi.org/10.1145/3661167.3661283
Web Address (URL) of Paperhttps://dl.acm.org/doi/10.1145/3661167.3661283
Web Address (URL) of Conference Proceedingshttps://dl.acm.org/doi/proceedings/10.1145/3661167
Conference/Event28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24)
Event Details
28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24)
Parent
International Conference on Evaluation and Assessment in Software Engineering
Delivery
In person
Event Date
18 to end of 21 Jun 2024
Event Location
Salerno, Italy
Event Web Address (URL)
AbstractIn recent years, the significant increase in enterprise data availability and the progress in Artificial Intelligence (AI) have enabled organizations to address real-world issues through Machine Learning (ML). In this regard, machine learning operations (MLOps) have been proven to be a beneficial strategy for evolving ML models from theoretical ideas to practical solutions of business sector issues. With the knowledge of MLOps being vast and scattered, this research work focuses on the application of MLOps methodologies in sophisticated operational technologies, prioritizing the enhancement of security measures. This research work also discusses the specific challenges in securing ML implementations in such settings and underscores the importance of robust MLOps strategies in ensuring effective security protocols. Moreover, it explains current practices and identified improvement areas, highlighting the importance of MLOps in overcoming obstacles and maximizing the value of ML in operational technology contexts. © 2024 ACM.
KeywordsBest Practices; Machine Learning; DevOps; Security; Operational Techn; MLOps; Continuous Deployment
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204611. Machine learning
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Byline AffiliationsUniversity of Southern Queensland
Inha University, Korea
Edinburgh Napier University, Untied States
Lappeenranta University of Technology (LUT), Finland
University of Faisalabad, Pakistan
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