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20212026
most citedA Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

56 citations · 233 across the 22 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

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.CL2024★ 1 cited

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…

cs.CR2024

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…

cs.CR2024★ 10 cited

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…

cs.CR2024★ 4 cited

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…