Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Alyas, Tahir; Javed, Iqra; Namoun, Abdallah; Tufail, Ali; Alshmrany, Sami; Tabassum, Nadia

doi:10.32604/cmc.2022.019836

Cited by 21 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analyzing malware samples gathered from honeypots was the major objective of our investigation on honeypot technologies. To do this, malware samples from both client and server honeypots are analyzed [12].…”

Section: Related Workmentioning

confidence: 99%

Multi-Cloud Integration Security Framework Using Honeypots

Alyas

Alissa

Alqahtani

et al. 2022

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Cloud Integration Security Framework Using Honeypots

Alyas

Alissa

Alqahtani

et al. 2022

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…It can manage virtual environments, handle port scanning data, choose an attack mode, and report the results of an attack, among other things. With the help of this platform, novice programmers may learn less code and get a general grasp of network security vulnerability testing [26].…”

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

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

“…The fungus disrupts the skin's normal pigmentation, resulting in small, discoloured areas. The hue of these areas is generally dimmer or darker [16]. Its clinical signs are as follows: white, pink, and dark-coloured spots appear primarily in the summer, infect only on the top layer of the skin, and the rash may appear on the trunk.…”

Section: Figure 4: Scalp Ringwormmentioning

confidence: 99%

Innovative Fungal Disease Diagnosis System Using Convolutional Neural燦etwork

Alyas¹,

Alissa²,

Mohammad³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections are found in almost every part of the natural world. When an invasive fungus takes over a body region and overwhelms the immune system, it causes fungal infections in people. Another primary goal of this study was to create a Convolutional Neural Network (CNN)-based technique for detecting and classifying various types of fungal diseases. There are numerous fungal illnesses, but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis (IFDD) system of Candidiasis and Tinea Infections. This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings. To identify and categorize fungal infections, deep machine learning techniques are utilized. A CNN architecture was created, and it produced a promising outcome to improve the proposed system accuracy. The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards. Our CNN-Based can detect fungal diseases through medical images; earmarked IFDD system has a predictive performance of 99.6% accuracy.

show abstract

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Cited by 21 publications

References 23 publications

Multi-Cloud Integration Security Framework Using Honeypots

Multi-Cloud Integration Security Framework Using Honeypots

Container Performance and Vulnerability Management for Container Security Using Docker Engine

Innovative Fungal Disease Diagnosis System Using Convolutional Neural燦etwork

Contact Info

Product

Resources

About