A GAN-Based Framework for Robust DDoS Attack Detection
WGAN-GP-generated adversarial DDoS traffic augments training data, improving detection resilience against evasion attempts.
Researchers built a DDoS detection framework combining Random Forests, deep neural ensembles, and Transformer-based models trained on CICDDoS2019 with synthetic adversarial flows generated by a Wasserstein GAN with gradient penalty. Hybrid datasets of benign, malicious, and generated traffic taught models more generalizable decision boundaries. Experiments showed improved accuracy and resilience against unseen adversarial traffic, validated on real-world generated flows.
- WGAN-GP synthesizes adversarial DDoS flows for augmentation
- Tests Random Forests, neural ensembles, Transformer models
- Baseline training uses the CICDDoS2019 dataset
- Improved resilience against unseen adversarial traffic
From the original article
1 min read at arxiv.orgThe availability and consistency of online services remain vulnerable due to Distributed Denial of Service (DDoS) attacks. These attacks are evolving by adopting more complex strategies to evade traditional network security systems. Despite the effectiveness of machine learning models in detecting DDoS traffic, targeted adversarial attacks can degrade their classification accuracy.
By Makram Chehayeb · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.