Boosting 6G Network Security Using a High-Performance Adaptive Threat Detection Algorithm (ATDA)

Authors

  • S. Muthumarilakshmi
  • G. Mahalakshmi
  • R. Poornima Lakshmi
  • C. S. Dhanalakshmi

DOI:

https://doi.org/10.52783/jns.v14.2929

Keywords:

Elliptic Curve Cryptography, Adaptive Threat Detection Algorithm

Abstract

With the advent of 6G communications, the demand for robust security mechanisms has become paramount to safeguard against increasingly sophisticated cyber threats. This research article introduces an innovative Adaptive Threat Detection Algorithm (ATDA) designed to enhance security in 6G communication networks. The ATDA leverages adaptive strategies and real-time data analysis to detect and mitigate potential security breaches more effectively. To validate the efficacy of the ATDA, a comprehensive comparative analysis was conducted against four well-established security algorithms: Intrusion Detection System (IDS) algorithms, Machine Learning-based Anomaly Detection algorithms, the Zero Trust Security Model, and Elliptic Curve Cryptography (ECC). The quantitative evaluation, utilizing advanced simulation tools and real-world application scenarios, demonstrates that the ATDA significantly outperforms traditional algorithms in terms of response time, overall threat mitigation capabilities, and detection accuracy. The results underscore the potential of ATDA to set a new benchmark in 6G communication security, offering a highly reliable solution for pre-empting and countering cyberattacks. This research article provides a detailed assessment of the ATDA's performance, paving the way for its adoption in future communication networks to ensure robust and resilient security infrastructure.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

A. S. Ali, R. L. Smith, and H. H. Kim, “Real-time Threat Detection in 6G Networks Using Adaptive Machine Learning Models,” IEEE Transactions on Network and Service Management, vol. 21, no. 1, pp. 45-56, Jan. 2024.

J. B. Chen, S. A. Patel, and D. M. Lee, “An Overview of Data Preprocessing Techniques for Cybersecurity Applications,” IEEE Access, vol. 12, pp. 12045-12060, Apr. 2024.

M. C. Wilson and E. R. Gomez, “Adaptive Learning Techniques for Anomaly Detection in Network Security,” IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 87-99, Feb. 2024.

F. L. Zhang and X. Q. Wang, “Machine Learning Approaches for Real-time Threat Classification in Cloud Environments,” IEEE Transactions on Cloud Computing, vol. 12, no. 4, pp. 789-802, Oct. 2023.

L. M. Robinson, K. A. Turner, and H. Y. Lee, “Evaluation of Threat Detection Models in Large-scale Networks,” IEEE Transactions on Information Forensics and Security, vol. 19, no. 3, pp. 235-247, Mar. 2023.

P. R. Gupta, S. P. Singh, and R. D. Kumar, “Machine Learning-Based Threat Detection: A Comprehensive Review,” IEEE Access, vol. 11, pp. 45012-45031, Aug. 2023.

T. J. Anderson, M. C. Clark, and N. H. Davis, “Efficient Noise Removal Techniques for Network Traffic Data,” IEEE Transactions on Computational Social Systems, vol. 11, no. 1, pp. 11-22, Jan. 2024.

A. N. Patel and V. T. Ramirez, “Optimizing Feature Extraction for Threat Detection in High-Speed Networks,” IEEE Transactions on Network and Service Management, vol. 21, no. 2, pp. 65-78, Apr. 2024.

D. W. Jackson and E. M. Brown, “Real-time Alert Generation Systems for Network Security,” IEEE Transactions on Network and Service Management, vol. 21, no. 3, pp. 123-135, Jul. 2024.

B. H. Martinez, J. L. Moore, and C. A. Lopez, “Adaptive Threat Detection Algorithms for Evolving Cybersecurity Threats,” IEEE Transactions on Cybernetics, vol. 54, no. 1, pp. 89-102, Jan. 2023.

R. A. Patel, K. L. Smith, and N. D. Wilson, “Integration of Machine Learning and Adaptive Systems for Network Security,” IEEE Transactions on Information Forensics and Security, vol. 19, no. 4, pp. 349-362, Apr. 2024.

S. B. Patel and J. W. Turner, “Advanced Data Normalization Techniques for Cybersecurity Applications,” IEEE Transactions on Network and Service Management, vol. 21, no. 4, pp. 141-153, Aug. 2024.

C. T. Williams and R. E. Hernandez, “Evaluating Machine Learning Models for Threat Detection in Real-time,” IEEE Access, vol. 12, pp. 13456-13472, Jul. 2024.

L. Q. Zhang, P. R. Thomas, and M. J. Clarke, “Feature Extraction Methods for Improved Threat Detection Accuracy,” IEEE Transactions on Cloud Computing, vol. 13, no. 2, pp. 221-233, May 2023.

M. J. Johnson and K. T. Evans, “Dynamic Threat Detection and Alert Generation Systems,” IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 101-115, Dec. 2023.

F. K. Singh, N. M. Patel, and A. B. Murphy, “The Role of Adaptive Learning in Network Security,” IEEE Transactions on Information Forensics and Security, vol. 19, no. 5, pp. 378-389, May 2024.

G. S. Kumar and R. C. Turner, “Machine Learning Techniques for Real-time Threat Classification and Management,” IEEE Access, vol. 13, pp. 14567-14580, Oct. 2023.

18. H. J. Clarke and D. P. Lee, “Advanced Noise Reduction Techniques in Cybersecurity Data Processing,” IEEE Transactions on Computational Social Systems, vol. 12, no. 1, pp. 23-34, Feb. 2024.

R. T. Harris, L. Y. Chen, and S. J. Moore, “Adaptive Threat Detection in 6G Networks: Challenges and Solutions,” IEEE Transactions on Network and Service Management, vol. 21, no. 5, pp. 175-188, Nov. 2024.

K. A. Clark and J. M. Martinez, “Innovative Approaches for Threat Detection Using Machine Learning in High-Speed Networks,” IEEE Transactions on Cloud Computing, vol. 13, no. 3, pp. 301-314, Aug. 2023.

Downloads

Published

2025-04-02

How to Cite

1.
Muthumarilakshmi S, Mahalakshmi G, Lakshmi RP, Dhanalakshmi CS. Boosting 6G Network Security Using a High-Performance Adaptive Threat Detection Algorithm (ATDA). J Neonatal Surg [Internet]. 2025Apr.2 [cited 2025Oct.1];14(5):238-4. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/2929