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MasterHead

Hi, I'm Francesco 👋

I'm a PhD Student in Brain, Mind and Computer Science at the University of Padua with a Master's degree in Cybersecurity. Here, I am part of the Security and Privacy (SPRITZ) research group. My research interests lie primarily in Automotive Security, focusing on Machine Learning and Deep Learning applications. I am also interested in Adversarial Attacks, Cyber Threat Intelligence, and Quantum Cryptography and Computing.

🔎 About Me

📖 | PhD Brain, Mind and Computer Science (2022 - Present)
🎓 | MSc Cybersecurity (2020 - 2022)
🎓 | BSc Information Engineering (2017 - 2020)
🏭 | Internship @ Leonardo (March 2022 - June 2022)

📚 Latest Publications

  • Emad Efatinasab, Francesco Marchiori, Alessandro Brighente, Mirco Rampazzo, and Mauro Conti. 2024. 21st Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA '24). Preprint: https://arxiv.org/abs/2403.17494
  • Francesco Marchiori and Mauro Conti. 2024. CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems. In Proceedings of the 2024 ACM Workshop on Wireless Security and Machine Learning (WiseML '24). Association for Computing Machinery, New York, NY, USA. Preprint: https://arxiv.org/abs/2404.04648
  • Filippo Perrina, Francesco Marchiori, Mauro Conti, and Nino Vincenzo Verde. 2023. AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation. 2023 IEEE International Conference on Big Data (Big Data '23), Sorrento, Italy, 2023. https://doi.org/10.1109/BigData59044.2023.10386116
  • Francesco Marchiori and Mauro Conti. 2023. Your Battery Is a Blast! Safeguarding Against Counterfeit Batteries with Authentication. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS '23). Association for Computing Machinery, New York, NY, USA, 105–119. https://doi.org/10.1145/3576915.3623179
  • Marco Alecci, Mauro Conti, Francesco Marchiori, Luca Martinelli, and Luca Pajola. 2023. Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability. In Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses (RAID '23). Association for Computing Machinery, New York, NY, USA, 315–329. https://doi.org/10.1145/3607199.3607227
  • Francesco Marchiori, Mauro Conti, and Nino Vincenzo Verde. 2023. STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports. In Proceedings of the 18th International Conference on Availability, Reliability and Security (ARES '23). Association for Computing Machinery, New York, NY, USA, Article 3, 1–11. https://doi.org/10.1145/3600160.3600182

💻 Languages and Tools

Python

TensorFlow

PyTorch

Java

C

C++

HTML

CSS

JavaScript

MatLab

Jupyter

VS Code

Linux

Bash



📬 Find Me

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📈 GitHub Stats

Francesco Marchiori's Github Stats

Francesco Marchiori's Projects

agir icon agir

Automating Cyber Threat Intelligence Reporting with Natural Language Generation

canederli icon canederli

On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems

cve-2021-3156 icon cve-2021-3156

Visualization, Fuzzing, Exploit and Patch of Baron Samedit Vulnerability

dcauth icon dcauth

Safeguarding Against Counterfeit Batteries with Authentication

deepfashion-gan icon deepfashion-gan

Generative Adversarial Network on DeepFashion Dataset at a base level

dumb icon dumb

Formalizing Attacker Scenarios for Adversarial Transferability

f5spam icon f5spam

Python tool to check changes in a website

ganzone icon ganzone

Machine Learning supported Lo-Fi Music Generator

mhackiori icon mhackiori

Repository for the README file of the profile

stixnet icon stixnet

A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports

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