Distributed Denial of Service Attack Detection Model for Peer to Peer (P2P) Networks
S Madhesh · International Journal for Research in Applied Science and Engineering Technology · 2025
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.