A Scalable Deep Learning-Metaheuristic Approach for DDoS Attack  Mitigation

Mrs. Manjula HT,Dr. Jyoti Metan

A Scalable Deep Learning-Metaheuristic Approach for DDoS Attack Mitigation

Authors

Mrs. Manjula HT,Dr. Jyoti Metan

Keywords:

Distributed denial-of-service; Developed Battle Royale Optimization Algorithm; Optimized Cybernet Model; Cross-channel normalization; Cybersecurity resilience.

Abstract

Distributed denial-of-service (DDoS) attacks, which inundate targeted systems with trafficfrom several sources, pose a significant threat to computer networks and systems. Detecting these attacks in real-time

References

H.T.Manjula,Neha Mangla An Approach to on stream DD0S blitz detection using machinelearningalgorithmsMaterialsElsevierToday:Proceedingshttps://doi.org/10.1016/j.matpr.2021.0 7.280

H.T.Manjula,Neha Mangla An Effectual Cram DDos Blitz Tools and Approach via Hadoop Framework,Volume 15 Issue 8 2020 Syebold report ISSN No:1533-9211

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Published

2024-03-10

How to Cite

Mrs. Manjula HT,Dr. Jyoti Metan. (2024). A Scalable Deep Learning-Metaheuristic Approach for DDoS Attack Mitigation. Journal of Computational Analysis and Applications (JoCAAA), 33(2), 1432–1451. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2806