Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security.
dc.contributor.author | Qayyum, A | |
dc.contributor.author | Ijaz, A | |
dc.contributor.author | Usama, M | |
dc.contributor.author | Iqbal, W | |
dc.contributor.author | Qadir, J | |
dc.contributor.author | Elkhatib, Y | |
dc.contributor.author | Al-Fuqaha, A | |
dc.date.accessioned | 2021-04-28T14:26:10Z | |
dc.date.available | 2020-10-08 | |
dc.date.available | 2021-04-28T14:26:10Z | |
dc.date.issued | 2020-11 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/71554 | |
dc.description.abstract | With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions-attacks and defenses-related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation. | en_US |
dc.format.extent | 587139 - ? | |
dc.language | eng | |
dc.publisher | Frontiers | en_US |
dc.relation.ispartof | Front Big Data | |
dc.rights | This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | Machine Learning as a Service | en_US |
dc.subject | attacks | en_US |
dc.subject | cloud machine learning security | en_US |
dc.subject | cloud-hosted machine learning models | en_US |
dc.subject | defenses | en_US |
dc.subject | machine learning security | en_US |
dc.subject | systematic review | en_US |
dc.title | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security. | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2020 Qayyum, Ijaz, Usama, Iqbal, Qadir, Elkhatib and Al-Fuqaha. | |
dc.identifier.doi | 10.3389/fdata.2020.587139 | |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/33693420 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | 3 | en_US |
dcterms.dateAccepted | 2020-10-08 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
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