SCIENCEBRING   /   HAMBURG, GERMANY
Login Register
International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
Submit Manuscript ↗

Search the Journal
Articles Published:

Synergistic Integration of Edge Intelligence, Generative AI, And Blockchain For Robust Security in Next-Generation 6G Communication Networks

Vol. 6 No. 02 (2026): Volume 06 Issue 02 Pages 127-136
Authors
  • Martina Sterling
    Department of Electrical Engineering and Computer Science, University of Melbourne, Australia
RESEARCH OVERVIEW

Abstract

The transition from 5G to 6G communication networks represents a paradigm shift from mere data connectivity to pervasive, ubiquitous intelligence. This research article explores the multi-faceted integration of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLM), with Edge Computing and Blockchain technology to address the escalating complexities of modern network infrastructures. As 6G aims to support ultra-low latency and massive device connectivity, traditional centralized security frameworks become obsolete. This study investigates the role of Edge Intelligence in decentralizing computational loads and the application of LLMs in semantic communication to optimize bandwidth. Furthermore, the paper provides an exhaustive analysis of cybersecurity threats, such as Distributed Denial of Service (DDoS) attacks and multi-layer cyber-physical intrusions, proposing a unified defense mechanism that leverages Blockchain for data integrity and Memristor-based neural networks for hardware-level efficiency. By synthesizing current literature on model compression, energy harvesting, and synchronizing neural networks under attack, this research outlines a comprehensive framework for the future of intelligent, secure, and energy-efficient 6G ecosystems. The findings suggest that a layered approach-combining semantic-aware transmission with decentralized edge security-is essential for the resilience of next-generation digital twins and autonomous systems.

Keywords

  • 6G Technology
  • Edge Intelligence
  • Large Language Models
  • Blockchain Security
SOURCES & CITATIONS

References

1. Abd Elaziz, M., Al-qaness, M. A., Dahou, A., et al. (2024). Evolution Toward Intelligent Communications: Impact of Deep Learning Applications on the Future of 6g Technology. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
2. Afifi, H., Pochaba, S., Boltres, A., et al. (2024). Machine Learning With Computer Networks: Techniques, Datasets, and Models. IEEE Access.
3. Alwahedi, F., Aldhaheri, M. A., Ferrag, M. A., Battah, A., and Tihanyi, N. (2024). Machine Learning Techniques for IoT Security: Current Research and Future Vision With Generative AI and Large Language Models. Internet of Things and Cyber-Physical Systems.
4. Cao, Y., and Cao, Y. (2022). Synchronization of multiple neural networks with reaction–diffusion terms under cyber–physical attacks. Knowledge-Based Systems.
5. Cao, Y., Cao, Y., Wen, S., Huang, T., and Zeng, Z. (2019). Passivity analysis of delayed reaction–diffusion memristor-based neural networks. Neural Networks.
6. Dantas, P. V., Silva, S. D. W., Jr., Cordeiro, L. C., and Carvalho, C. B. (2024). A Comprehensive Review of Model Compression Techniques in Machine Learning. Applied Intelligence.
7. Faheem, M., and Al-Khasawneh, M. A. (2024). Multilayer Cyberattacks Identification and Classification Using Machine Learning in Internet of Blockchain (Iobc)-Based Energy Networks. Data in Brief.
8. IBM. (2024). Internet of Things for insights from connected devices. Reference Architecture.
9. Jiang, F., Peng, Y., Dong, L., et al. (2024). Large Language Model Enhanced Multi-Agent Systems for 6G Communications. IEEE Wireless Communications.
10. Jia, Y., Zhong, F., Alrawais, A., Gong, B., and Cheng, X. (2020). Flowguard: An intelligent edge defense mechanism against IoT ddos attacks. IEEE Internet Things Journal.
11. Kumar, P., Kumar, R., Gupta, G.P., and Tripathi, R. (2021). A distributed framework for detecting ddos attacks in smart contract-based blockchain-IoT systems by leveraging fog computing. Transactions on Emerging Telecommunications Technologies.
12. Kumar, V., Das, A.K., and Sinha, D. (2021). UIDS: A unified intrusion detection system for IoT environment. Evolutionary Intelligence.
13. Prakash, R. V., Gowtham, M., Dutt, A., Pravallika, B., and Chakravarthi, M. K. (2024). Experimental Study on Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks. IEEE.
14. Ravi, N., and Shalinie, S.M. (2020). Learning-driven detection and mitigation of ddos attack in IoT via SDN-cloud architecture. IEEE Internet Things Journal.
15. Sumaiya Thaseen, I., Saira Banu, J., Lavanya, K., Rukunuddin Ghalib, M., and Abhishek, K. (2021). An integrated intrusion detection system using correlation-based attribute selection and artificial neural network. Transactions on Emerging Telecommunications Technologies.
16. Varanasi, S. R., Valiveti, S. S. S., Adnan, M., Faruk, M. I., Hossain, M. J., & Manik, M. M. T. G. (2026). Cross-Domain standardization and secure edge intelligence for Real-Time digital twin deployments in Next-Generation communication systems. IEEE Communications Standards Magazine, 1–6. https://doi.org/10.1109/mcomstd.2026.3662187
17. Wen, S., Xiao, S., Yang, Y., Yan, Z., Zeng, Z., and Huang, T. (2018). Adjusting learning rate of memristor-based multilayer neural networks via fuzzy method. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
18. Yin, D., Zhang, L., and Yang, K. (2018). A ddos attack detection and mitigation with software-defined internet of things framework. IEEE Access.
19. Zarpelão, B.B., Miani, R.S., Kawakani, C.T., and de Alvarenga, S.C. (2017). A survey of intrusion detection in internet of things. Journal of Network and Computer Applications.
20. Zhao, Y., Yue, Y., Hou, S., Cheng, B., and Huang, Y. (2024). LaMoSC: Large Language Model-Driven Semantic Communication System for Visual Transmission. IEEE Transactions on Cognitive Communications and Networking.
21. Zhu, J., Hu, C., Khezri, E., and Ghazali, M. M. M. (2024). Edge Intelligence-Assisted Animation Design With Large Models: A Survey. Journal of Cloud Computing.
DISCOVER RELATED RESEARCH

Similar Articles

Continue exploring the collection ↗
1-10 of 515