Secure Deployment of Cloud Integrated Cybersecurity Applications: A Comprehensive Cloud Security Model

Mulloly, Anjali Pappachan (2023) Secure Deployment of Cloud Integrated Cybersecurity Applications: A Comprehensive Cloud Security Model. Masters thesis, Dublin, National College of Ireland.

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Abstract

The current research is built to analyze and detect the buffer overflow attack on mail using the cloud-integrated cyber security model. However, the model also assesses datasets, identifies buffer overflow incidents and provides accuracy for email-based attacks. Mainly, the use of machine learning techniques is considered an intriguing way of addressing these cloud security challenges. These algorithms implement pattern recognition, anomaly detection, and predictive analytics to analyze and detect any threats. Many difficulties were encountered when developing the model, including integrating AWS with machine learning. These issues were resolved, although the model could only function once the secret key was created and applied to it. This model will establish a connection with AWS at that point. In addition to this, the main objective of the research was to implement machine learning algorithms for detecting buffer overflow attacks over the mail and then integrate the detection results into a cloud-based cybersecurity model that is connected with AWS. Furthermore, the methodology focuses on gathering data sets from CVE details and online portals. Data processing also assists in eliminating the special character and null values and sklearn. Feature selection, validation and training of the model, and AWS performance have been included. It makes use of an integrated machine learning-based model that aids in the analysis of the buffer overflow assault that took place and contributed to the problems with hostile activity. Datasets have been utilized in this model to examine buffer overflows on CSM mail. This will first examine the buffer overflow on the CSM mail, and if the model is successful in obtaining an accuracy of roughly 90% precision in detecting the buffer overflow, it will then forward the result to the cloud-integrated architecture, which aids in demonstrating the outcome that the network attack is taking place. If so, this indicates that the model can identify cloud buffer overflow attacks. The study has been answering research questions related to the particular security needs of different cloud-integrated cybersecurity apps, considering issues. Additionally, suggested CSM, along with current cloud security models and frameworks, includes its benefits and distinguishing features to resolve the security problems of cloud-integrated cybersecurity applications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email McLaughlin, Eugene 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 > Algebra > Algorithms > Computer algorithms 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:	Ciara O'Brien
Date Deposited:	21 Apr 2025 11:09
Last Modified:	21 Apr 2025 11:09
URI:	https://norma.ncirl.ie/id/eprint/7448

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