A fine-tuned end-to-end model based on adversarial training for entity recognition in cybersecurity

Puntos clave

• The model achieves a significant increase in entity recognition accuracy compared to traditional methods, enhancing cybersecurity measures.
• Accuracy improved by 20% during testing on complex datasets specifically designed for cybersecurity applications.
• Analysis of adversarial training techniques enhances the model's ability to identify threats in real-time scenarios.
• This work highlights the potential for adaptive technology in cybersecurity, necessitating further exploration in diverse environments.