6 papers
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…
Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection
José Gonçalves, Miguel Silva, Eva Maia +1
The application of Artificial Intelligence has become a powerful approach to detecting software vulnerabilities. However, effective vulnerability detection relies on accurately cap…
Evaluating LLaMA 3.2 for Software Vulnerability Detection
José Gonçalves, Miguel Silva, Bernardo Cabral +5
Deep Learning (DL) has emerged as a powerful tool for vulnerability detection, often outperforming traditional solutions. However, developing effective DL models requires large amo…
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…