CloudLens: Modeling and Detecting Cloud Security Vulnerabilities
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910760407400448 |
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| author | Kazdagli, Mikhail Tiwari, Mohit Kumar, Akshat |
| author_facet | Kazdagli, Mikhail Tiwari, Mohit Kumar, Akshat |
| contents | Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. With their growing popularity, concerns about security vulnerabilities are increasing. To address this, first, we provide a formal model, called CloudLens, that expresses relations between different cloud objects such as users, datastores, security roles, representing access control policies in cloud systems. Second, as access control misconfigurations are often the primary driver for cloud attacks, we develop a planning model for detecting security vulnerabilities. Such vulnerabilities can lead to widespread attacks such as ransomware, sensitive data exfiltration among others. A planner generates attacks to identify such vulnerabilities in the cloud. Finally, we test our approach on 14 real Amazon AWS cloud configurations of different commercial organizations. Our system can identify a broad range of security vulnerabilities, which state-of-the-art industry tools cannot detect. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CloudLens: Modeling and Detecting Cloud Security Vulnerabilities Kazdagli, Mikhail Tiwari, Mohit Kumar, Akshat Cryptography and Security Artificial Intelligence Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. With their growing popularity, concerns about security vulnerabilities are increasing. To address this, first, we provide a formal model, called CloudLens, that expresses relations between different cloud objects such as users, datastores, security roles, representing access control policies in cloud systems. Second, as access control misconfigurations are often the primary driver for cloud attacks, we develop a planning model for detecting security vulnerabilities. Such vulnerabilities can lead to widespread attacks such as ransomware, sensitive data exfiltration among others. A planner generates attacks to identify such vulnerabilities in the cloud. Finally, we test our approach on 14 real Amazon AWS cloud configurations of different commercial organizations. Our system can identify a broad range of security vulnerabilities, which state-of-the-art industry tools cannot detect. |
| title | CloudLens: Modeling and Detecting Cloud Security Vulnerabilities |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2402.10985 |