A Review of Machine Learning-based Security in Cloud Computing
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arXiv
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| Format: | Preprint |
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2023
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| author | Babaei, Aptin Kebria, Parham M. Dalvand, Mohsen Moradi Nahavandi, Saeid |
| author_facet | Babaei, Aptin Kebria, Parham M. Dalvand, Mohsen Moradi Nahavandi, Saeid |
| contents | Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of security risks, including threats to availability, integrity, and confidentiality. To address these challenges, Machine Learning (ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the need for human intervention in identifying and resolving security issues. With the ability to analyze vast amounts of data, and make high-accuracy predictions, ML can transform the way CSPs approach security. In this paper, we will explore some of the most recent research in the field of ML-based security in Cloud Computing. We will examine the features and effectiveness of a range of ML algorithms, highlighting their unique strengths and potential limitations. Our goal is to provide a comprehensive overview of the current state of ML in cloud security and to shed light on the exciting possibilities that this emerging field has to offer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_04911 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Review of Machine Learning-based Security in Cloud Computing Babaei, Aptin Kebria, Parham M. Dalvand, Mohsen Moradi Nahavandi, Saeid Cryptography and Security Artificial Intelligence Machine Learning Networking and Internet Architecture Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of security risks, including threats to availability, integrity, and confidentiality. To address these challenges, Machine Learning (ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the need for human intervention in identifying and resolving security issues. With the ability to analyze vast amounts of data, and make high-accuracy predictions, ML can transform the way CSPs approach security. In this paper, we will explore some of the most recent research in the field of ML-based security in Cloud Computing. We will examine the features and effectiveness of a range of ML algorithms, highlighting their unique strengths and potential limitations. Our goal is to provide a comprehensive overview of the current state of ML in cloud security and to shed light on the exciting possibilities that this emerging field has to offer. |
| title | A Review of Machine Learning-based Security in Cloud Computing |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2309.04911 |