AI-Driven Multi-Factor Authentication and Dynamic Trust Management for Securing Massive Machine Type Communication in 6G Networks

Authors

  • P. Hussain Basha Assistant professor, Department of Computer Science and Engineering, PACE Institute of Technology & Science.Ongole.AP, India
  • G. Prathyusha Department of Computer Science ,Sri Padmavati Mahila Visvavidyalayam. Tirupati
  • Dunna Nikitha Rao Department of computer science , Sri Padmavati mahila visvavidyalayam, Tirupati, India
  • Vellaturi Gopikrishna Asst. Professor, Department of Information Technology, MLR Institute of Technology, Hyderabad
  • Prasadu Peddi Associate Professor , Dept of CSE & IT , Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan.India
  • V. Saritha Department of Computer Science and Engineering ,Sri Padmavati Mahila Visvavidyalayam, Tirupati, India

Keywords:

AI-driven authentication, dynamic trust management, 6G networks, massive machine type communication, behavioural profiling, deep learning, multi-factor authentication, IoT security

Abstract

With the advent of the 6G era on the horizon, the widespread adoption of massive machine-type communication (MTC) presents novel security concerns about preserving data integrity and confidentiality during transmission across a vast network of interconnected devices. To tackle these challenges, this research paper presents a novel AI-based multi-factor authentication and dynamic trust management system designed to enhance machine-type communications (MTC) security in 6G networks. This study presents a novel behavioural profiling model that utilizes artificial intelligence techniques to effectively accommodate machine-type devices’ varied capabilities and resource limitations. Simulated data is created to replicate diverse scenarios, encompassing variations in network conditions, device attributes, environmental influences, application workloads, and security protocols. The efficacy of the suggested solution is assessed by conducting six simulation rounds, wherein the outcomes reveal diverse levels of accuracy in implementing multi-factor authentication (MFA), ranging from 0.47 to 0.55. The system demonstrates stability in various simulation scenarios, indicating its ability to adapt to dynamic network environments and device behaviour. The utilization of AI-driven multi-factor authentication and dynamic trust management system shows significant potential in enhancing the security of Machine Type Communications (MTC) within the context of 6G networks. The potential of this technology to effectively address security threats in the dynamic and evolving 6G environment is attributed to its robustness and adaptability. It is advisable to conduct additional refinements and validate the system in real-world settings to enhance its performance and guarantee smooth integration into practical deployment scenarios.

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Published

02.09.2023

How to Cite

Basha, P. H. ., Prathyusha, G. ., Rao, D. N. ., Gopikrishna, V. ., Peddi , P. ., & Saritha, V. . (2023). AI-Driven Multi-Factor Authentication and Dynamic Trust Management for Securing Massive Machine Type Communication in 6G Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 361–374. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3422

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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