João Vitorino

Orcid: 0000-0002-4968-3653

Affiliations:
  • School of Engineering, Polytechnic of Porto (ISEP/IPP), Porto, Portugal


According to our database1, João Vitorino authored at least 14 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Adversarial Evasion Attack Efficiency against Large Language Models.
CoRR, 2024

Efficient Network Traffic Feature Sets for IoT Intrusion Detection.
CoRR, 2024

Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection.
CoRR, 2024

2023
SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection.
Comput. Secur., November, 2023

Towards adversarial realism and robust learning for IoT intrusion detection and classification.
Ann. des Télécommunications, August, 2023

Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review.
CoRR, 2023

An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection.
Proceedings of the Foundations and Practice of Security - 16th International Symposium, 2023

Unravelling Network-Based Intrusion Detection: A Neutrosophic Rule Mining and Optimization Framework.
Proceedings of the Computer Security. ESORICS 2023 International Workshops, 2023

From Data to Action: Exploring AI and IoT-Driven Solutions for Smarter Cities.
Proceedings of the Distributed Computing and Artificial Intelligence, 2023

Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection.
Proceedings of the Artificial Intelligence in Medicine, 2023

2022
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection.
Future Internet, 2022

A Low-Cost Multi-Agent System for Physical Security in Smart Buildings.
CoRR, 2022

A Multi-policy Framework for Deep Learning-based Fake News Detection.
Proceedings of the Distributed Computing and Artificial Intelligence, 2022

2021
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection.
Proceedings of the Foundations and Practice of Security - 14th International Symposium, 2021


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