Fabio Pierazzi

Associate Professor in Computer Science at University College London

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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

Nov 1, 2024 I joined UCL Computer Science as Associate Professor in Information Security.
Oct 20, 2024 Shae got a paper accepted at ARTMAN (co-located with ACSAC).
Sep 20, 2024 Jacopo successfully passed his Ph.D. viva, with the usual minor amendments!
Sep 15, 2024 New papers accepted at two CCS Workshops: AISec24 and CPSIoTSec24.
Sep 6, 2024 Marcus successfully passed his Ph.D. viva, with the usual minor amendments!

selected publications

2024

  1. 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

2023

  1. 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
  2. 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

2022

  1. 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

2020

  1. 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

2019

  1. 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