EPFL IC SPRING
Office BC 262
CH 1015, Lausanne
- Theory and foundations of Machine Learning
- Traffic analysis, Machine Learning in adversarial conditions, and their formal analysis
- Methods for confident prediction in supervised learning and anomaly detection (e.g., Conformal Predictors)
Have a look at a list of recent projects.
I co-founded and play with the CTF team TU6PM.
I am a (happy) OpenBSD and FreeBSD user, and I would highly recommend you become too.
|Feb 28, 2019||Our paper, “F-BLEAU: Fast Black-box Leakage Estimation”, has been accepted by the IEEE Symposium on Security and Privacy, 2019. It shows how to use ML methods for measuring the information leakage of a black-box system in a practical yet theoretically sound manner.|
|Dec 16, 2018||The code of fbleau for measuring the leakage of black box systems is now online and available for installation via crates.io.|
|Nov 6, 2018||A list of semester projects for EPFL MSc/PhD students is available at https://spring.epfl.ch/en/projects.|
|Sep 3, 2018||Work on Conformal Predictors' ensebles accepted by the Machine Learning journal (read more)|
Some recent projects.
F-BLEAU: Fast Black-box Leakage Estimation In IEEE Symposium on Security and Privacy (S&P) 2019
The Bayes Security Measure Work in progress 2018
Majority vote ensembles of conformal predictors Machine Learning 2018
Exchangeability martingales for selecting features in anomaly detection In Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications 2018
Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses Proceedings on Privacy Enhancing Technologies 2017 Best student paper
Website Fingerprinting Defenses at the Application Layer Proceedings on Privacy Enhancing Technologies 2017
Conformal Clustering and Its Application to Botnet Traffic In Statistical Learning and Data Sciences (SLDS) 2015 Best student paper
Bots detection by Conformal Clustering MSc thesis, Royal Holloway University of London 2014
Selected invited talks.
Measuring the Security of Machine Learning models 2019 Third ITU/WHO Workshop on "Artificial Intelligence for Health"
Measuring the Leakage of a Black-box using Machine Learning 2018 Alan Turing Institute, London
Bayes, not Naïve: Provable Security of Website Fingerprinting Defenses 2017 ISG Seminar, Royal Holloway University of London, UK
On the Security Against Machine Learning-based Attacks 2017 CDT Showcase, Evelyn Sharp Centre, Sunningdale Park
Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses 2017 Security Seminar, University of Cambridge
Applications of Conformal Prediction in Information Security Problems 2016 CDT Showcase, Windsor Great Park, UK
Conformal Clustering and Bots Traffic 2015 CPRML Workshop 2015, Hyderabad, India
Research Engineer, HP Labs Security Lab, Bristol (August-November 2017)
Supervisors: Jonathan Griffin, Adrian Baldwin
Research Visitor, École Polytechnique, Paris (May; November 2017)
Supervisors: Prof. Catuscia Palamidessi, Kostas Chatzikokolakis
Research Intern, Cornell Tech (June-September 2016)
Supervisor: Prof. Thomas Ristenpart
I have been teaching assistant for R programming for the courses on Machine Learning and Data Analysis at Royal Holloway University of London (2014-17). I was teaching assistant for the courses on C programming and Linear Algebra and Geometry at University of Pavia (2011-12).
- 2017, Best Paper: Andreas Pfitzmann Best Student Paper Award at PETS: “Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses”
- 2017, First place at Capture The Flag (CTF) security challenge organised by NCC Group at the Cambridge2Cambridge event
- 2015, Best Paper: Best student paper award sponsored by HP at SLDS conference: “Conformal Clustering and Its Application to Botnet Traffic”
- 2014, Best Finalist: Best MSc in Big Data finalist in memory of Prof. Alexey Chervonenkis (Royal Holloway University of London)
I am a postdoc researcher at EPFL (Switzerland) with an EcoCloud grant, collaborating with Carmela Troncoso at the SPRING lab since October 2018. I obtained a PhD in Machine Learning and Information Security from Royal Holloway University of London with the CDT, where I was supervised by Alex Gammerman, and advised by Kenny Paterson. I received an MSc in Machine Learning from Royal Holloway University of London in 2014, and a BSc in Mechatronics and Computer Engineering from University of Pavia in 2013.
My current research aims at measuring systems’ leakage by using methods from the Machine Learning theory; I applied this to side channel attacks (e.g., traffic analysis). I also worked on extending methods for confident prediction (e.g., Conformal Predictors), particularly in the context of clustering, anomaly detection, and classifiers ensembling.