10 citations · 14 across the 5 of their papers we have counts for
6 papers
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…
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…
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…