Self-Adapting Security Monitoring in Eucalyptus Cloud Environment

Mahmood, Salman; Yahaya, Nor Adnan; Rakhshandeh, Hasan; Hussain, Saqib; Malik, Mazhar Hussain; Sarker, Kamal Uddin

doi:10.14569/ijacsa.2023.0140310

Cited by 1 publication

(2 citation statements)

References 25 publications

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“…It is characterized by a global cloud infrastructure, implying that the services offered within this framework have a wide-ranging geographical reach. Tenants within this environment have the authority to govern their VMs, affording them control over a networked ecosystem that is integral to their operations and functionalities [25][26][27]. The mechanism used for this study in the Eucalyptus Cloud Environment is shown in Figure 1 [25][26][27].…”

Section: Iaas Cloud Environmentmentioning

confidence: 99%

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Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment

Mahmood,

Hasan,

Yahaya

et al. 2024

Knowledge

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This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

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Section: Iaas Cloud Environmentmentioning

confidence: 99%

“…Tenants within this environment have the authority to govern their VMs, affording them control over a networked ecosystem that is integral to their operations and functionalities [25][26][27]. The mechanism used for this study in the Eucalyptus Cloud Environment is shown in Figure 1 [25][26][27]. Design Principles…”

Section: Iaas Cloud Environmentmentioning

confidence: 99%