Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Nadeem, Muhammad; Ali, Arshad; Riaz, Saman; Zahra, SyedaWajiha; Rashid, Muhammad H.; Band, Shahab S.; Mosavi, Amir

doi:10.32604/cmc.2023.028796

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…The modular API is designed to seamlessly integrate a diverse array of machine learning models to enhance threat detection within a private cloud environment. The selected models, including random forest [28,29], support vector machines [28,30], neural networks [31,32], k-nearest neighbors [33,34], decision tree [35,36], stochastic gradient descent [37,38], naive Bayes [39,40], logistic regression [41,42], gradient boosting [41,[43][44][45] and AdaBoost [46], each bring unique capabilities to the framework. Random forest's robustness is rigorously assessed for identifying network anomalies, while support vector machines focus on precise threat identification with minimal false positives.…”

Section: Machine Learning Model Evaluationmentioning

confidence: 99%

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: Machine Learning Model Evaluationmentioning

confidence: 99%

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

Mahmood,

Hasan,

Yahaya

et al. 2024

Knowledge

View full text Add to dashboard Cite

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“…Ancaman terhadap keamanan siber semakin kompleks. Aplikasi perlu dilindungi dari berbagai serangan siber yang mungkin terjadi, seperti serangan DDoS, SQL Injection, serta eksploitasi kerentanan [4].…”

Section: Pendahuluanunclassified

Pemanfaatan layanan cloud computing dan docker container untuk meningkatkan kinerja aplikasi web

Ekaputra,

Affandi

2023

J. of Information System and Application Development

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This research aims to analyze and implement the utilization of Content Delivery Networks (CDN) and cloud computing services in web applications using Nginx Proxy Manager and Docker containers. This research uses experimental methods and case studies to analyze the performance of web applications before and after using CDN services and implementing the application in a cloud computing environment using Docker containers. Next, Nginx Proxy Manager is used to provide a user-friendly interface for configuring and managing Nginx proxy servers. Docker containers are used to manage web applications and proxy servers. In the CDN and cloud computing environment, Cloudflare Google Cloud Platform will be used as service providers. The results of this research indicate a significant improvement in the security and reliability of web applications after implementing CDN services and adopting a cloud computing environment. Additionally, this research provides insight into how to use Nginx Proxy Manager and Docker container to manage web applications efficiently and effectively in a cloud computing environment.

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Cited by 2 publications

References 27 publications

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

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

Pemanfaatan layanan cloud computing dan docker container untuk meningkatkan kinerja aplikasi web

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