Hierarchical Ml Model for Distributed Ddos Attack Classification and Hyperparameter Tuning
Year: 2025
Type: article
Abstract: The security of networks is seriously threatened by sophisticated DDoS attacks. In order to increase the precision of distant detection and classification, this article describes a hierarchical machine learning methodology. The system starts with a core layer that detects fundamental issues, then expands classifications into multiple DDoS assault categories. By eliminating extraneous data and increasing efficiency, the approach uses mutual information and correlation-based filters to identify the most relevant qualities. Bayesian optimization for hyperparameters enables improved detection compared to conventional techniques like grid and random search. Experiments using datasets like CIC-DDoS2019 show that this approach lowers false positives while significantly improving accuracy, recall, and F1-score. This cutting-edge and adaptable technology works in real time and can defend modern network systems against dynamic DDoS attacks.
Author: INTERNATIONAL JOURNAL OF ADVANCED RESEARCH & INNOVATIONS
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6912210612
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