GraphGAN Framework Proposed for DDoS Detection in Next-Generation Networks
A recent preprint on arXiv (arXiv:2608.17352) introduces GraphGAN, a generative adversarial network that utilizes graph structures to identify Distributed Denial-of-Service (DDoS) attacks in advanced networks. This research highlights the necessity for cognitive detection methods that surpass mere traffic analysis, tackling issues of class imbalance and fluctuating conditions. By transforming sequential network flows into k-nearest neighbor graphs through sliding windows, GraphGAN preserves both feature similarities and temporal correlations. The system features a generator that creates realistic samples of the minority class and employs a Graph Convolutional Network (GCN)-based discriminator to distinguish between real and synthetic data. Additionally, a GCN classifier is trained on the balanced dataset for effective detection, aiming to bolster AI-enhanced network security against emerging cyber threats.
Key facts
- Paper titled 'Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks' is available on arXiv.
- The study proposes a Graph-based Generative Adversarial Network (GraphGAN) as the cognitive detection engine for DDoS attacks.
- GraphGAN addresses severe class imbalance and non-stationary conditions in network traffic.
- Sequential flows are converted into k-nearest neighbor graphs using sliding windows to preserve feature similarity and temporal dependencies.
- The generator learns the distribution of DDoS attacks to synthesize realistic minority samples.
- A Graph Convolutional Network (GCN)-based discriminator distinguishes real from synthetic graph data.
- A separate GCN classifier is trained on the balanced dataset for detection.
- The arXiv preprint number is 2608.17352.
Entities
Institutions
- arXiv