LATIDIA · Ciberseguridad

Debiasing adversarial de modelos de aprendizaje automático para mejorar la seguridad de la red contra ataques DDoS

arXiv:2609.36167v1 Tipo de anuncio: nuevo Resumen: Los ataques distribuidos de denegación de servicio son una amenaza creciente para la infraestructura de red, y las nuevas técnicas, incluido el uso de IA generativa, los hacen más difíciles de detectar. Tr

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arXiv:2609.36167v1 Announce Type: new Abstract: Distributed Denial of Service attacks are a growing threat to network infrastructure, and new techniques, including the use of generative AI, make them harder to detect. Traditional detection systems, such as rule based firewalls, often fail to identify these evolving attack patterns. In this study, we propose a new method for detecting DDoS attacks by combining synthetic data generation using Generative Adversarial Networks with a Random Forest classifier. The GAN generated data showed 80.3 percent cosine similarity to real traffic, which helped the model learn underlying traffic patterns more effectively. To address imbalances in the data, especially in packet related features, we applied adversarial debiasing.