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 Type | Paper |
| Authors | Ahmad, Tazeem, Adnan, Mohd, Rafi, Saima, Akbar, Muhammad Azeem and Anwar, Ayesha |
| Journal or Proceedings Title | Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24) |
| Journal Citation | pp. 662-667 |
| Number of Pages | 6 |
| Year | 2024 |
| Publisher | Association for Computing Machinery (ACM) |
| Place of Publication | United States |
| ISBN | 9798400717017 |
| Digital Object Identifier (DOI) | https://doi.org/10.1145/3661167.3661283 |
| Web Address (URL) of Paper | https://dl.acm.org/doi/10.1145/3661167.3661283 |
| Web Address (URL) of Conference Proceedings | https://dl.acm.org/doi/proceedings/10.1145/3661167 |
| Conference/Event | 28th 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) |
| Abstract | In 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. |
| Keywords | Best Practices; Machine Learning; DevOps; Security; Operational Techn; MLOps; Continuous Deployment |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4611. Machine learning |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University 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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https://research.usq.edu.au/item/z860y/mlops-enabled-security-strategies-for-next-generation-operational-technologies
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