Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system

Tabassum, Nadia; Khan, Muhammad Saleem; Abbas, Sagheer; Alyas, Tahir; Athar, Atifa

doi:10.4108/eai.13-7-2018.159408

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Furthermore, the dataset ensures completeness by having no missing data. In the proposed research, Random Forest and K-nearest neighbor are utilized to train the model [33].…”

Section: Trainingmentioning

confidence: 99%

Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning

Humaira Naeem

2023

IJECI

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Network security in IoT-based cloud computing can benefit greatly from the application of machine learning techniques. IoT devices introduce unique security challenges with their large-scale deployments and diverse nature. Machine learning can help address these challenges by analyzing IoT network traffic, detecting anomalies, identifying potential threats, and enhancing overall network security. The security of cloud networks is validated using binary classification to detect attacks. Random forest classifiers achieved an accuracy of 96%, while K nearest classifier had an accuracy of 93% and a precision value of 0.96. The proposed model ensures security of big data against intrusion attacks on the network. Although machine learning techniques can be powerful for protecting cloud computing networks, challenges still need to be addressed before widespread adoption. Understanding the potential and limitations of machine learning approaches to network security canhelp researchers and practitioners develop more effective strategies for safeguarding their systems in an increasingly interconnected world. Network security of big data in cloud computing can be enhanced by applying machine learning techniques. Machine learning algorithms can analyze large amounts of data to detect patterns, anomalies, and potential security threats. Here are several ways machine learning can be utilized to improve network security in the context of big data and cloud computing.

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“…Furthermore, the dataset ensures completeness by having no missing data. In the proposed research, Random Forest and K-nearest neighbor are utilized to train the model [33].…”

Section: Trainingmentioning

confidence: 99%

Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning

Humaira Naeem

2023

IJECI

View full text Add to dashboard Cite

show abstract

“…It is also notable that in a single con guration, only rewall, intrusion detection system, intrusion prevention system, or honeypots are not successful or not too much impactful. erefore, it is recommended to engage multiple mechanisms to trade o the weaknesses and strengths [19].…”

Section: Related Workmentioning

confidence: 99%

Multi-Cloud Integration Security Framework Using Honeypots

Alyas

Alissa

Alqahtani

et al. 2022

Mobile Information Systems

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This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts.

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“…One such enables the Docker concept applied on Hadoop Streaming, which reduces the setup time and configuration errors. Overall, there are areas of improvement yet, providing elasticity and good performance [24].…”

Section: Security Vulnerabilities In Containerizationmentioning

confidence: 99%

Container Performance and Vulnerability Management for Container Security Using Docker Engine

Alyas

Ali

Khan

et al. 2022

Security and Communication Networks

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Containers have evolved to support microservice architecture as a low-cost alternative to virtual machines. Containers are increasingly prevalent in the virtualization landscape because of better working; containers can bear considerably less overhead than the conventional hypervisor-based component virtual machines. However, containers directly communicate with the host kernel, and attackers can co-locate containers in the host system quicker than virtual machines. This causes significant security issues in container technology. The security hardening system is currently targeted at implementing universal access management regulations that make it difficult to assess the required procedure for accessing containers. Security mechanisms include an explicit awareness of the purpose and actions of the container and entail manual interaction and configuration. A user-friendly container protection scheme implemented an access policy to comply with its anticipated and legitimate application performance. In this study, container technology constraints have been overcome by proposing a unique Docker-sec mechanism. Docker-sec uses four mechanisms; the original collection has been improved during container runtime by additional rules that constrain the capacity of the container, further representing the applications in practice, file system, processes, network isolation, and vulnerability scanning of Docker images over different workload. Different vulnerabilities have been scanned with a CVE severity level. Results showed that inter-container communication with the system is more secure containers from zero vulnerabilities with an overhead of 3.45%.

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Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system

Cited by 6 publications

References 13 publications

Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning

Analysis of Network Security in IoT-based Cloud Computing Using Machine Learning

Multi-Cloud Integration Security Framework Using Honeypots

Container Performance and Vulnerability Management for Container Security Using Docker Engine

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