activity
20222024
most citedTowards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification

48 citations · 50 across the 8 of their papers we have counts for

collaborators

5 papers

cs.CL20241 cited

Adversarial Evasion Attack Efficiency against Large Language Models

João Vitorino, Eva Maia, Isabel Praça

Large Language Models (LLMs) are valuable for text classification, but their vulnerabilities must not be disregarded. They lack robustness against adversarial examples, so it is pe…

cs.CR2024

Efficient Network Traffic Feature Sets for IoT Intrusion Detection

Miguel Silva, João Vitorino, Eva Maia +1

The use of Machine Learning (ML) models in cybersecurity solutions requires high-quality data that is stripped of redundant, missing, and noisy information. By selecting the most r…

cs.LG20231 cited

Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection

João Vitorino, Lourenço Rodrigues, Eva Maia +2

Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairmen…

cs.CR202348 cited

Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification

João Vitorino, Isabel Praça, Eva Maia

The Internet of Things (IoT) faces tremendous security challenges. Machine learning models can be used to tackle the growing number of cyber-attack variations targeting IoT systems…

cs.CR2022

A Low-Cost Multi-Agent System for Physical Security in Smart Buildings

Tiago Fonseca, Tiago Dias, João Vitorino +2

Modern organizations face numerous physical security threats, from fire hazards to more intricate concerns regarding surveillance and unauthorized personnel. Conventional standalon…