56 citations · 178 across the 20 of their papers we have counts for
20 papers
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…
SCoPE: Evaluating LLMs for Software Vulnerability Detection
José Gonçalves, Tiago Dias, Eva Maia +1
In recent years, code security has become increasingly important, especially with the rise of interconnected technologies. Detecting vulnerabilities early in the software developme…
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer
Tiago Dias, Eva Maia, Isabel Praça
Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy tes…
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…
Herb-Drug Interactions: A Holistic Decision Support System in Healthcare
Andreia Martins, Eva Maia, Isabel Praça
Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore,…
TestLab: An Intelligent Automated Software Testing Framework
Tiago Dias, Arthur Batista, Eva Maia +1
The prevalence of software systems has become an integral part of modern-day living. Software usage has increased significantly, leading to its growth in both size and complexity.…
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…
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 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…
Incomplete Transition Complexity of Basic Operations on Finite Languages
Eva Maia, Nelma Moreira, Rogério Reis
The state complexity of basic operations on finite languages (considering complete DFAs) has been in studied the literature. In this paper we study the incomplete (deterministic) s…