Distributed Denial of Service Attack Detection Model for Peer to Peer (P2P) Networks

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.