Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security
<p dir="ltr">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 increa...
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2020
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| _version_ | 1864513511838711808 |
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| author | Adnan Qayyum (16875936) |
| author2 | Aneeqa Ijaz (18949486) Muhammad Usama (3629090) Waleed Iqbal (10294253) Junaid Qadir (16494902) Yehia Elkhatib (566867) Ala Al-Fuqaha (4434340) |
| author2_role | author author author author author author |
| author_facet | Adnan Qayyum (16875936) Aneeqa Ijaz (18949486) Muhammad Usama (3629090) Waleed Iqbal (10294253) Junaid Qadir (16494902) Yehia Elkhatib (566867) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Adnan Qayyum (16875936) Aneeqa Ijaz (18949486) Muhammad Usama (3629090) Waleed Iqbal (10294253) Junaid Qadir (16494902) Yehia Elkhatib (566867) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2020-11-12T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fdata.2020.587139 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Securing_Machine_Learning_in_the_Cloud_A_Systematic_Review_of_Cloud_Machine_Learning_Security/26144125 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Machine Learning as a Service cloud-hosted machine learning models machine learning security cloud machine learning security systematic review attacks defenses |
| dc.title.none.fl_str_mv | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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—<i>attacks</i> and <i>defenses</i>—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.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Big Data<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fdata.2020.587139" target="_blank">https://dx.doi.org/10.3389/fdata.2020.587139</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_523eb77c28dbe03abcc15a03dd089294 |
| identifier_str_mv | 10.3389/fdata.2020.587139 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26144125 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning SecurityAdnan Qayyum (16875936)Aneeqa Ijaz (18949486)Muhammad Usama (3629090)Waleed Iqbal (10294253)Junaid Qadir (16494902)Yehia Elkhatib (566867)Ala Al-Fuqaha (4434340)Information and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningMachine Learning as a Servicecloud-hosted machine learning modelsmachine learning securitycloud machine learning securitysystematic reviewattacksdefenses<p dir="ltr">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—<i>attacks</i> and <i>defenses</i>—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.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Big Data<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fdata.2020.587139" target="_blank">https://dx.doi.org/10.3389/fdata.2020.587139</a></p>2020-11-12T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fdata.2020.587139https://figshare.com/articles/journal_contribution/Securing_Machine_Learning_in_the_Cloud_A_Systematic_Review_of_Cloud_Machine_Learning_Security/26144125CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/261441252020-11-12T09:00:00Z |
| spellingShingle | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security Adnan Qayyum (16875936) Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Machine Learning as a Service cloud-hosted machine learning models machine learning security cloud machine learning security systematic review attacks defenses |
| status_str | publishedVersion |
| title | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| title_full | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| title_fullStr | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| title_full_unstemmed | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| title_short | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| title_sort | Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security |
| topic | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Machine Learning as a Service cloud-hosted machine learning models machine learning security cloud machine learning security systematic review attacks defenses |