Year: 2025
Type: article
Abstract: Distributed Denial of Service (DDoS) attacks are among the most prevalent and disruptive forms of cyberattacks, aiming to make a machine or network resource unavailable to its intended users. Traditional rule-based detection systems often fail to adapt to evolving attack strategies. This paper presents a machine learning-based hybrid framework for DDoS detection using Support Vector Machines (SVM), Bidirectional Long Short-Term Memory networks (BiLSTM), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The system uses NetFlow-inspired features extracted from live traffic captured in a virtualized Mininet environment. SVM is employed for supervised classification, BiLSTM for time-series based sequence learning, and DBSCAN for unsupervised anomaly detection. The results demonstrate that this hybrid approach provides robust detection accuracy, reduced false positives, and adaptability to unknown attacks.
Author: Madhesh S
Institution: Puducherry Technological University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409924709