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Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics.

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Názov: Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics.
Autori: Morić, Zlatan, Dakić, Vedran, Kapulica, Ana, Regvart, Damir
Zdroj: Electronics (2079-9292); Nov2024, Vol. 13 Issue 22, p4546, 32p
Predmety: DIGITAL forensics, FORENSIC sciences, VIRTUAL machine systems, CYBERTERRORISM, COMPUTER network security
Abstrakt: This article delves into Microsoft Azure's cyber forensic capabilities, focusing on the unique challenges in cloud security incident investigation. Cloud services are growing in popularity, and Azure's shared responsibility model, multi-tenant nature, and dynamically scalable resources offer unique advantages and complexities for digital forensics. These factors complicate forensic evidence collection, preservation, and analysis. Data collection, logging, and virtual machine analysis are covered, considering physical infrastructure restrictions and cloud data transience. It evaluates Azure-native and third-party forensic tools and recommends methods that ensure effective investigations while adhering to legal and regulatory standards. It also describes how AI and machine learning automate data analysis in forensic investigations, improving speed and accuracy. This integration advances cyber forensic methods and sets new standards for future innovations. Unified Audit Logs (UALs) in Azure are examined, focusing on how Azure Data Explorer and Kusto Query Language (KQL) can effectively parse and query large datasets and unstructured data to detect sophisticated cyber threats. The findings provide a framework for other organizations to improve forensic analysis, advancing cloud cyber forensics while bridging theoretical practices and practical applications, enhancing organizations' ability to combat increasingly sophisticated cybercrime. [ABSTRACT FROM AUTHOR]
Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Complementary Index
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  Data: Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics.
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  Data: <searchLink fieldCode="AR" term="%22Morić%2C+Zlatan%22">Morić, Zlatan</searchLink><br /><searchLink fieldCode="AR" term="%22Dakić%2C+Vedran%22">Dakić, Vedran</searchLink><br /><searchLink fieldCode="AR" term="%22Kapulica%2C+Ana%22">Kapulica, Ana</searchLink><br /><searchLink fieldCode="AR" term="%22Regvart%2C+Damir%22">Regvart, Damir</searchLink>
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  Data: Electronics (2079-9292); Nov2024, Vol. 13 Issue 22, p4546, 32p
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  Data: <searchLink fieldCode="DE" term="%22DIGITAL+forensics%22">DIGITAL forensics</searchLink><br /><searchLink fieldCode="DE" term="%22FORENSIC+sciences%22">FORENSIC sciences</searchLink><br /><searchLink fieldCode="DE" term="%22VIRTUAL+machine+systems%22">VIRTUAL machine systems</searchLink><br /><searchLink fieldCode="DE" term="%22CYBERTERRORISM%22">CYBERTERRORISM</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+network+security%22">COMPUTER network security</searchLink>
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  Data: This article delves into Microsoft Azure's cyber forensic capabilities, focusing on the unique challenges in cloud security incident investigation. Cloud services are growing in popularity, and Azure's shared responsibility model, multi-tenant nature, and dynamically scalable resources offer unique advantages and complexities for digital forensics. These factors complicate forensic evidence collection, preservation, and analysis. Data collection, logging, and virtual machine analysis are covered, considering physical infrastructure restrictions and cloud data transience. It evaluates Azure-native and third-party forensic tools and recommends methods that ensure effective investigations while adhering to legal and regulatory standards. It also describes how AI and machine learning automate data analysis in forensic investigations, improving speed and accuracy. This integration advances cyber forensic methods and sets new standards for future innovations. Unified Audit Logs (UALs) in Azure are examined, focusing on how Azure Data Explorer and Kusto Query Language (KQL) can effectively parse and query large datasets and unstructured data to detect sophisticated cyber threats. The findings provide a framework for other organizations to improve forensic analysis, advancing cloud cyber forensics while bridging theoretical practices and practical applications, enhancing organizations' ability to combat increasingly sophisticated cybercrime. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/electronics13224546
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        Text: English
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      – SubjectFull: VIRTUAL machine systems
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      – SubjectFull: CYBERTERRORISM
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      – SubjectFull: COMPUTER network security
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              Text: Nov2024
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