56 citations · 178 across the 20 of their papers we have counts for
12 papers · 1 filter
A Novel Approach to Network Traffic Analysis: the HERA tool
Daniela Pinto, Ivone Amorim, Eva Maia +1
Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machi…
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…
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…
Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference
Ivone Amorim, Eva Maia, Pedro Barbosa +1
The use of Neural Networks (NNs) for sensitive data processing is becoming increasingly popular, raising concerns about data privacy and security. Homomorphic Encryption (HE) has t…
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…
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…
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…