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.
Theo Chow, Mario D’Onghia, Lorenz Linhardt, Zeliang Kan, Daniel Arp, Lorenzo Cavallaro, and Fabio Pierazzi , "Beyond the TESSERACT: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers" , In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2026
@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},booktitle={{IEEE} Conference on Secure and Trustworthy Machine Learning ({SaTML})},}
Shae McFadden, Myles Foley, Mario D’Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, and Fabio Pierazzi , "DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift" , In Proc. 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26), 2026
@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},}
Yigitcan Kaya, Yizheng Chen, Marcus Botacin, Shoumik Saha, Fabio Pierazzi, Lorenzo Cavallaro, David Wagner, and Tudor Dumitras , "ML-Based Behavioral Malware Detection Is Far From a Solved Problem" , In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2025
@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},}
ACM TOPS
Jacopo Cortellazzi, Feargus Pendlebury, Daniel Arp, Erwin Quiring, Fabio Pierazzi, and Lorenzo Cavallaro , "Intriguing properties of adversarial ML attacks in the problem space [Extended Version]" , In ACM Transactions on Privacy and Security (TOPS), 2025
@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},}
Ilias Tsingenopoulos, Jacopo Cortellazzi, Branislav Bošanský, Simone Aonzo, Davy Preuveneers, Wouter Joosen, Fabio Pierazzi, and Lorenzo Cavallaro , "How to Train your Antivirus: RL-based Hardening through the Problem Space" , In Proc. of the International Symposium on Research in Attacks, Intrusions and Defenses (RAID), 2024
@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})},}
Shae McFadden, Marcello Maugeri, Chris Hicks, Vasilis Mavroudis, and Fabio Pierazzi , "Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL" , In Proc. of the IEEE Workshop on Deep Learning Security and Privacy (DLSP), 2024
@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},}
Theo Chow, Zeliang Kan, Lorenz Linhardt, Lorenzo Cavallaro, Daniel Arp, and Fabio Pierazzi , "Drift Forensics of Malware Classifiers" , In Proc. of the ACM Workshop on Artificial Intelligence and Security (AISec), 2023
@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},}
Giovanni Apruzzese, Hyrum S Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, and Kevin Roundy , "“Real Attackers Don’t Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice" , In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023 Position Paper
@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>}}
Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck , "Dos and don’ts of machine learning in computer security" , In Proc. of USENIX Security Symposium, 2022 Distinguished Paper Award
@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>}}
Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro , "Intriguing properties of adversarial ML attacks in the problem space" , In IEEE Symposium on Security and Privacy (S&P), 2020
@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},}
Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, and Lorenzo Cavallaro , "TESSERACT: Eliminating experimental bias in malware classification across space and time" , In Proc. of USENIX Security Symposium, 2019
@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},}
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