Fabio Pierazzi

Office 109

169 Euston Rd.

UCL, London

I am invested into understanding how AI can be used to improve systems security tasks, with a particular emphasis on settings in which attackers adapt quickly to new defenses. In this context, I am currently exploring how to measure, mitigate and prevent concept drift in ML-based detection systems; how to measure and improve robustness of systems to adversarial attacks while taking into account problem-space constraints (such as preserving semantics of modified code); and how to explain what these systems are doing, both from an attacker’s and a defender’s perspective.

I also care deeply about the practicality of our proposed solutions, and on understanding implications and limitations of using AI in the context of computer security. To this purpose, I also regularly engage and collaborate with industry. I mostly work on malware analysis and network traffic, but I am becoming more interested in understanding inner workings of AI and ML models to improve their trustworthiness in more general security scenarios.

I am always looking for motivated students and collaborators passionate about these topics. If you are interested in joining my team, or even just visiting, have a look here.

news

Jul 22, 2026	Shae’s paper has been accepted to USENIX Security 2026!
Mar 09, 2026	PhD Opportunity for Home/UK only (deadline: Apr 10, 2026).
Mar 05, 2026	Xin Fan’s paper has been accepted to IEEE EuroS&P!
Dec 23, 2025	CDT Cyber-Physical Risk PhD opportunity for Home/UK only (deadline: Feb 15).
Dec 10, 2025	Theo and Mario’s paper has been accepted to SaTML26!

selected publications

IEEE SaTML

Beyond the TESSERACT: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers

Theo Chow, Mario D’Onghia, Lorenz Linhardt, Zeliang Kan, Daniel Arp, Lorenzo Cavallaro, and Fabio Pierazzi

In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2026

Bib PDF Code

@inproceedings{chow2025breaking,
  title = {{Beyond the TESSERACT: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers}},
  author = {Chow, Theo and D'Onghia, Mario and Linhardt, Lorenz and Kan, Zeliang and Arp, Daniel and Cavallaro, Lorenzo and Pierazzi, Fabio},
  year = {2026},
  url = {https://arxiv.org/abs/2506.23814},
  booktitle = {{IEEE} Conference on Secure and Trustworthy Machine Learning ({SaTML})},
}

AAAI

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift

Shae McFadden, Myles Foley, Mario D’Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, and Fabio Pierazzi

In Proc. 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26), 2026

Bib PDF Code

@inproceedings{mcfadden2025drmd,
  title = {{DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift}},
  author = {McFadden, Shae and Foley, Myles and D'Onghia, Mario and Hicks, Chris and Mavroudis, Vasilios and Paoletti, Nicola and Pierazzi, Fabio},
  booktitle = {Proc. 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26)},
  year = {2026},
}

USENIX Sec

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

Shae McFadden, Myles Foley, Elizabeth Bates, Ilias Tsingenopoulos, Sanyam Vyas, Vasilios Mavroudis, Chris Hicks, and Fabio Pierazzi

In Proc. of USENIX Security Symposium, 2026

arXiv Bib PDF

@inproceedings{mcfadden2026sok,
  title = {SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity},
  author = {McFadden, Shae and Foley, Myles and Bates, Elizabeth and Tsingenopoulos, Ilias and Vyas, Sanyam and Mavroudis, Vasilios and Hicks, Chris and Pierazzi, Fabio},
  booktitle = {Proc. of {USENIX} Security Symposium},
  year = {2026},
}

IEEE EuroS&P

KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs

Xin Fan Guo, Xinran Zheng, Albert Merono Penuela, Lorenzo Cavallaro, Sergio Maffeis, and Fabio Pierazzi

In Proc. 11th IEEE European Symposium on Security and Privacy (EuroS&P), 2026

Bib PDF Code

@inproceedings{guo2026knowml,
  title = {KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs},
  author = {Guo, Xin Fan and and Xinran Zheng and Penuela, Albert Merono and Cavallaro, Lorenzo and Maffeis, Sergio and Pierazzi, Fabio},
  year = {2026},
  booktitle = {Proc. 11th IEEE European Symposium on Security and Privacy (EuroS\&P)},
}

RAID

How to Train your Antivirus: RL-based Hardening through the Problem Space

Ilias Tsingenopoulos, Jacopo Cortellazzi, Branislav Bošanský, Simone Aonzo, Davy Preuveneers, Wouter Joosen, Fabio Pierazzi, and Lorenzo Cavallaro

In Proc. of the International Symposium on Research in Attacks, Intrusions and Defenses (RAID), 2024

Bib PDF

@inproceedings{ilias2024train,
  title = {How to Train your Antivirus: RL-based Hardening through the Problem Space},
  author = {Tsingenopoulos, Ilias and Cortellazzi, Jacopo and Bošanský, Branislav and Aonzo, Simone and Preuveneers, Davy and Joosen, Wouter and Pierazzi, Fabio and Cavallaro, Lorenzo},
  year = {2024},
  booktitle = {Proc. of the International Symposium on Research in Attacks, Intrusions and Defenses ({RAID})},
}

IEEE SaTML

ML-Based Behavioral Malware Detection Is Far From a Solved Problem

Yigitcan Kaya, Yizheng Chen, Marcus Botacin, Shoumik Saha, Fabio Pierazzi, Lorenzo Cavallaro, David Wagner, and Tudor Dumitras

In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2025

Bib PDF Website

@inproceedings{kaya2025satml,
  title = {ML-Based Behavioral Malware Detection Is Far From a Solved Problem},
  author = {Kaya, Yigitcan and Chen, Yizheng and Botacin, Marcus and Saha, Shoumik and Pierazzi, Fabio and Cavallaro, Lorenzo and Wagner, David and Dumitras, Tudor},
  booktitle = {{IEEE} Conference on Secure and Trustworthy Machine Learning ({SaTML})},
  year = {2025},
}

DLSP

Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL

Shae McFadden, Marcello Maugeri, Chris Hicks, Vasilis Mavroudis, and Fabio Pierazzi

In Proc. of the IEEE Workshop on Deep Learning Security and Privacy (DLSP), 2024

Bib PDF Code

@inproceedings{mcfadden2024wendigo,
  title = {Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL},
  author = {McFadden, Shae and Maugeri, Marcello and Hicks, Chris and Mavroudis, Vasilis and Pierazzi, Fabio},
  booktitle = {Proc. of the {IEEE} Workshop on Deep Learning Security and Privacy ({DLSP})},
  year = {2024},
}

AISec

Drift Forensics of Malware Classifiers

Theo Chow, Zeliang Kan, Lorenz Linhardt, Lorenzo Cavallaro, Daniel Arp, and Fabio Pierazzi

In Proc. of the ACM Workshop on Artificial Intelligence and Security (AISec), 2023

Bib PDF Code

@inproceedings{chow2023driftforensics,
  title = {Drift Forensics of Malware Classifiers},
  author = {Chow, Theo and Kan, Zeliang and Linhardt, Lorenz and Cavallaro, Lorenzo and Arp, Daniel and Pierazzi, Fabio},
  booktitle = {Proc. of the {ACM} Workshop on Artificial Intelligence and Security ({AISec})},
  year = {2023},
}

USENIX Sec

Dos and don’ts of machine learning in computer security

Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck

In Proc. of USENIX Security Symposium, 2022

Distinguished Paper Award

arXiv Bib PDF Website

@inproceedings{ArpQuiPen+22,
  title = {Dos and don'ts of machine learning in computer security},
  author = {Arp, Daniel and Quiring, Erwin and Pendlebury, Feargus and Warnecke, Alexander and Pierazzi, Fabio and Wressnegger, Christian and Cavallaro, Lorenzo and Rieck, Konrad},
  booktitle = {Proc. of {USENIX} Security Symposium},
  year = {2022},
  note = {<br/><span class="badge badge-primary">Distinguished Paper Award</span>}
}

IEEE SaTML

“Real Attackers Don’t Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice

Giovanni Apruzzese, Hyrum S Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, and Kevin Roundy

In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023

Position Paper

Bib PDF Website

@inproceedings{apruzzese2023real,
  title = {{“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice}},
  author = {Apruzzese, Giovanni and Anderson, Hyrum S and Dambra, Savino and Freeman, David and Pierazzi, Fabio and Roundy, Kevin},
  booktitle = {{IEEE} Conference on Secure and Trustworthy Machine Learning ({SaTML})},
  pages = {339--364},
  year = {2023},
  organization = {IEEE},
  note = {<br/><span class="badge badge-warning">Position Paper</span>}
}

ACM TOPS

Intriguing properties of adversarial ML attacks in the problem space [Extended Version]

Jacopo Cortellazzi, Feargus Pendlebury, Daniel Arp, Erwin Quiring, Fabio Pierazzi, and Lorenzo Cavallaro

In ACM Transactions on Privacy and Security (TOPS), 2025

Bib Code Website

@inproceedings{cortellazzi2025intriguing,
  title = {Intriguing properties of adversarial ML attacks in the problem space [Extended Version]},
  author = {Cortellazzi, Jacopo and Pendlebury, Feargus and Arp, Daniel and Quiring, Erwin and Pierazzi, Fabio and Cavallaro, Lorenzo},
  booktitle = {{ACM} Transactions on Privacy and Security ({TOPS})},
  year = {2025},
  organization = {ACM},
}

IEEE S&P

Intriguing properties of adversarial ML attacks in the problem space

Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro

In IEEE Symposium on Security and Privacy (S&P), 2020

Bib PDF Code Website

@inproceedings{pierazzi2020intriguing,
  title = {Intriguing properties of adversarial ML attacks in the problem space},
  author = {Pierazzi, Fabio and Pendlebury, Feargus and Cortellazzi, Jacopo and Cavallaro, Lorenzo},
  booktitle = {{IEEE} Symposium on Security and Privacy ({S\&P})},
  pages = {1332--1349},
  year = {2020},
  organization = {IEEE},
}

USENIX Sec

TESSERACT: Eliminating experimental bias in malware classification across space and time

Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro

In Proc. of USENIX Security Symposium, 2019

Bib PDF Code Website

@inproceedings{pendlebury2019tesseract,
  title = {TESSERACT: Eliminating experimental bias in malware classification across space and time},
  author = {Pendlebury, Feargus and Pierazzi, Fabio and Jordaney, Roberto and Kinder, Johannes and Cavallaro, Lorenzo},
  booktitle = {Proc. of {USENIX} Security Symposium},
  year = {2019},
}