Authentication, access control and scalability models in Internet of Things Security–A review

Kokila, M; Reddy K, Srinivasa

doi:10.1016/j.csa.2024.100057

Cited by 2 publications

(2 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IoT sensors and devices are widely used in these systems, which creates several cyberattack weaknesses. Achieving data and ML model security is crucial to prevent unauthorized access and modification (Kokila and Reddy K, 2025).…”

Section: Protection and Privacymentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

show abstract

Section: Protection and Privacymentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

show abstract

“…Adaptive Multi-Factor Authentication (A-MFA) is an enhanced version of MFA that provides a method for allowing authorized users to access systems using different factors that change based on different considerations (Kokila & Reddy K, 2024). In other words, authentication factors include passwords, biometrics among others are selected adaptively by the authentication system based on the criteria of whether the user is trying to log in from within the system boundaries, or whether the user is trying to access during the organization's operational hours.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of System Access Security in The Implementation of Multi-Factor Authentication (MFA) in Educational Institutions

Cahyaningrum

2024

J. Pract. Computer Sci.

View full text Add to dashboard Cite

Information systems in educational institutions have the potential to be targeted by various parties, especially unauthorized parties, so they require a higher layer of security to protect sensitive data and secure user access. One solution that can be implemented is the implementation of Multi-Factor Authentication (MFA), a security approach that utilizes more than one authentication method to secure access to the system. MFA is a security method that requires more than one way to verify a user's identity when accessing a system, application, or service that aims to increase security by adding an additional layer of protection beyond using a single passphrase or password. This research aims to evaluate the effectiveness and impact of implementing MFA in the context of system access security in educational environments. The research methodology involves surveying system users, statistical analysis, and monitoring access activity to assess the extent to which MFA is successful in reducing security risks and protecting sensitive information. The research results show that the use of MFA in educational institutions can significantly increase system access security. Several obstacles that arise become challenges in solving problems regarding the use of MFA. Therefore, this article also examines recommendations for increasing the implementation of MFA in educational institutions. It is hoped that this research can contribute to the positive impact of MFA in improving system access security in the context of educational institutions by optimizing information technology in protecting data integrity and user access security.

show abstract

Authentication, access control and scalability models in Internet of Things Security–A review

Cited by 2 publications

References 95 publications

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Evaluation of System Access Security in The Implementation of Multi-Factor Authentication (MFA) in Educational Institutions

Contact Info

Product

Resources

About