Ramesha, Keerti (2023) Adaptive Cloud Access Security Broker. Masters thesis, Dublin, National College of Ireland.
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Abstract
Cloud Security threats are exponentially increasing with individuals and organizations moving their workloads to cloud. It is therefore necessary to monitor all cloud activities from the user end and the application end and enforce security mechanisms to safeguard the data on cloud. Cloud Access Security Brokers (CASB) monitor user activity, inspect the application accesses and warn administrators about potential threats. However, a static CASB solution can have a predefined response state to known threats and may be easily overpowered by various intrusions, malware, and data packets. It may not be well equipped to handle attacks in real time. The solution developed is an adaptive CASB which includes a machine learning model that adapts to real time scenarios as and when new data is added and responds to threats dynamically and regulate data accesses. The research shows that the result of the use of machine learning in CASB systems have the potential to improve the security and usability of cloud-based systems.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Sahni, Vikas UNSPECIFIED |
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
Divisions: | School of Computing > Master of Science in Cyber Security |
Depositing User: | Tamara Malone |
Date Deposited: | 04 May 2023 16:34 |
Last Modified: | 04 May 2023 16:34 |
URI: | https://norma.ncirl.ie/id/eprint/6541 |
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