8 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…
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…
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…
Revisiting Network Traffic Analysis: Compatible network flows for ML models
João Vitorino, Daniela Pinto, Eva Maia +2
To ensure that Machine Learning (ML) models can perform a robust detection and classification of cyberattacks, it is essential to train them with high-quality datasets with relevan…
Adversarially Robust and Interpretable Magecart Malware Detection
Pedro Pereira, José Gouveia, João Vitorino +2
Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving rob…