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20212025
most citedAdaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

43 citations · 50 across the 13 of their papers we have counts for

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cs.CR2024

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…

cs.CR2024

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…

cs.CR2024

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…

cs.CR202243 cited

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…

cs.CR2021

Machine Learning for Network-based Intrusion Detection Systems: an Analysis of the CIDDS-001 Dataset

José Carneiro, Nuno Oliveira, Norberto Sousa +2

With the increasing amount of reliance on digital data and computer networks by corporations and the public in general, the occurrence of cyber attacks has become a great threat to…