Deep Convolutional Neural Network Architecture for Real Time DDoS Attack Detection in High Speed Network Infrastructures Using Packet Level Behavioral Analysis

The proliferation of high-bandwidth network services has created unprecedented opportunities for large-scale Distributed Denial-of-Service (DDoS) attacks capable of overwhelming critical infrastructure within seconds. This paper presents a novel Deep Convolutional Neural Network (DCNN) architecture specifically designed for real-time DDoS attack detection in high-speed network environments operating at 10 Gbps and above. The proposed model employs a three-dimensional feature extraction mechanism that transforms raw packet headers into spatial-temporal representations, enabling the network to learn hierarchical attack patterns without manual feature engineering. Experimental validation performed on the UNSW-NB15 and CSE-CIC-IDS2018 datasets reveals that the proposed DCNN achieves 99.1% detection accuracy with an average inference time of 0.8 milliseconds per packet.