W. Fahs

Adaptive Uncertainty-Routed Analysis is introduced, a multimodal email threat detection system that analyzes both the content of an email and its embedded URLs and quantifies prediction uncertainty from a URL classifier.

The scalable and efficient solution against adversarial DDoS attacks, introduced in this work, paves the way towards more resilient and adaptive network defense systems that combine generative adversarial augmentation with recent advances in learning models.

Experimental results show that short browsing sessions are highly identifiable, while future navigation actions are highly predictable from long-term interaction structure combined with recent behavioral context, indicating that repeated interaction patterns remain the dominant predictive signal in the evaluated web-browsing setup.