56 citations · 233 across the 22 of their papers we have counts for
24 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…
SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cards
João Vitorino, Eva Maia, Isabel Praça +1
Due to the susceptibility of Artificial Intelligence (AI) to data perturbations and adversarial examples, it is crucial to perform a thorough robustness evaluation before any Machi…
Enhancing JavaScript Malware Detection through Weighted Behavioral DFAs
Pedro Pereira, José Gonçalves, João Vitorino +2
This work addresses JavaScript malware detection to enhance client-side web application security with a behavior-based system. The ability to detect malicious JavaScript execution…
Flow Exporter Impact on Intelligent Intrusion Detection Systems
Daniela Pinto, João Vitorino, Eva Maia +2
High-quality datasets are critical for training machine learning models, as inconsistencies in feature generation can hinder the accuracy and reliability of threat detection. For t…
Network Simulation with Complex Cyber-attack Scenarios
Tiago Dias, João Vitorino, Eva Maia +1
Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality…
Intelligent Green Efficiency for Intrusion Detection
Pedro Pereira, Paulo Mendes, João Vitorino +2
Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in te…
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…
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…
Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection
João Vitorino, Miguel Silva, Eva Maia +1
The growing cybersecurity threats make it essential to use high-quality data to train Machine Learning (ML) models for network traffic analysis, without noisy or missing data. By s…
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
João Vitorino, Miguel Silva, Eva Maia +1
As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes. To reliably compare the robust…
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
João Vitorino, Isabel Praça, Eva Maia
Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performe…
From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities
Tiago Dias, Tiago Fonseca, João Vitorino +3
The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential t…
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…
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…
Constrained Adversarial Learning for Automated Software Testing: a literature review
João Vitorino, Tiago Dias, Tiago Fonseca +2
It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickl…
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…
A Multi-Policy Framework for Deep Learning-Based Fake News Detection
João Vitorino, Tiago Dias, Tiago Fonseca +2
Connectivity plays an ever-increasing role in modern society, with people all around the world having easy access to rapidly disseminated information. However, a more interconnecte…
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
João Vitorino, Nuno Oliveira, Isabel Praça
Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading de…
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
João Vitorino, Rui Andrade, Isabel Praça +2
The digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for…