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Updated: Nov 14, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security.

Adnan Qayyum1, Aneeqa Ijaz2, Muhammad Usama1

  • 1Information Technology University (ITU), Lahore, Pakistan.

Frontiers in Big Data
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

Machine Learning as a Service (MLaaS) platforms face security risks due to cloud adoption. This review systematically analyzes attacks and defenses in cloud-hosted ML/DL models, highlighting research gaps.

Keywords:
Machine Learning as a Serviceattackscloud machine learning securitycloud-hosted machine learning modelsdefensesmachine learning securitysystematic review

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing Security

Background:

  • Machine Learning (ML) and Deep Learning (DL) are increasingly hosted on cloud platforms (MLaaS).
  • Outsourcing DL model training to cloud services is common due to high computational costs.
  • This widespread adoption creates significant security vulnerabilities for ML/DL systems.

Purpose of the Study:

  • To systematically review and evaluate existing literature on security aspects of cloud-hosted ML/DL models.
  • To analyze the landscape of attacks and defenses in MLaaS environments.
  • To identify limitations and future research directions in this domain.

Main Methods:

  • A systematic literature review was conducted.
  • Articles were analyzed based on their focus on attacks, defenses, or both.
  • A total of 31 relevant articles were identified and evaluated.

Main Results:

  • The review identified 19 articles focusing on attacks, six on defenses, and six on both.
  • There is a growing research interest in both attacking and defending MLaaS platforms.
  • The analysis revealed limitations and challenges in current research.

Conclusions:

  • The security of MLaaS platforms is a rapidly evolving research area.
  • Further investigation is needed to address identified limitations and open research issues.
  • A comprehensive understanding of attack vectors and defense mechanisms is crucial for secure MLaaS deployment.