12 papers
Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
Diana Magalhães, Eva Maia, João Vitorino +1
Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU res…
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
Tomás Pereira, João Vitorino, Eva Maia +1
Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable. An e…
Influence Factors on RAG Poisoning
Pedro Pereira, Eva Maia, Isabel Praça +1
Retrieval-Augmented Generation (RAG) systems enhance large language models by grounding responses in retrieved documents from external knowledge sources at inference time. However,…
Machine Learning Transferability for Malware Detection
César Vieira, João Vitorino, Eva Maia +1
Malware continues to be a predominant operational risk for organizations, especially when obfuscation techniques are used to evade detection. Despite the ongoing efforts in the dev…
Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption
Ivan Costa, Pedro Correia, Ivone Amorim +2
Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorp…
Binary and Multiclass Cyberattack Classification on GeNIS Dataset
Miguel Silva, Daniela Pinto, João Vitorino +4
The integration of Artificial Intelligence (AI) in Network Intrusion Detection Systems (NIDS) is a promising approach to tackle the increasing sophistication of cyberattacks. Howev…