Gustavo de Carvalho Bertoli

Employment

• Airbus (Germany) Research Scientist
  2024 - Present
• Airbus Defense and Space Cybersecurity Architect for Aerospace & Research
  2022 - 2024
• Embraer SA Product Development Engineer
  2013 - 2022

Education

• Instituto Tecnológico de Aeronáutica PhD
  2017 - 2023
• Instituto Tecnológico de Aeronáutica Master of Engineering
  2012 - 2015
• Universidade Estadual Paulista Júlio de Mesquita Filho - Câmpus de Bauru BS Electrical Engineering
  2012 - 2012

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• Anomaly-Flow: A Multi-Domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection
  IEEE Network 2026 DOI: 10.1109/MNET.2025.3567251
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• Design and implementation of intelligent packet filtering in IoT microcontroller-based devices
  Journal of Internet Services and Applications 2024 DOI: 10.5753/jisa.2024.3835
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• Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach
  Computers & Security 2023 DOI: 10.1016/j.cose.2023.103106
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• Combination of Models for Denial-Of-Service Classification over Different Networks
  Proceedings of the 11th Latin-American Symposium on Dependable Computing 2022 DOI: 10.1145/3569902.3570176
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• Avaliação da capacidade de generalização de IDS stateful utilizando aprendizado de máquina
  2022 DOI: 10.5753/sbseg.2022.225165
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• Improving detection of scanning attacks on heterogeneous networks with Federated Learning
  ACM SIGMETRICS Performance Evaluation Review 2022 DOI: 10.1145/3543146.3543172
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• Poster: Safe Gate – IoT system to support sanitary measures to combat COVID-19
  11th International Conference on the Internet of Things 2021 DOI: 10.1145/3494322.3494699
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• Classification of Denial of Service Attacks on Wi-Fi-based Unmanned Aerial Vehicle
  2021 10th Latin-American Symposium on Dependable Computing (LADC) 2021 DOI: 10.1109/ladc53747.2021.9672561
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• An End-to-End Framework for Machine Learning-Based Network Intrusion Detection System
  IEEE Access 2021 DOI: 10.1109/access.2021.3101188
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• Bridging the gap to real-world for network intrusion detection systems with data-centric approach
  2021 DOI: 10.48550/ARXIV.2110.13655
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