Fabio Pierazzi
Associate Professor in Information Security
Studies how AI improves systems security, including concept drift and adversarial robustness in ML-based detection.
Fabio Pierazzi's lab at UCL works on trustworthy & safe ML and recognition, detection & segmentation. 22 papers since 2023, 5 at top venues, mostly AAAI and CCS. 4 PhD students. 4 alumni, most often to Apple and Meta.
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 23%
- PhD students
- 4
- MS students
- 0
- alumni listed
- 4
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 2 · collecting
- 3 papers2025 · T1 0
- 7 papers2024 · T1 1
- 7 papers2023 · T1 2
T1 venuesOtherVenues: AAAI, CCS, ICRA, S&P, USENIX Security
Research focuspaper titles that use each topic's words
- Recognition, detection & segmentation4 papers
- adversarial4 papers
- Trustworthy & safe ML1 paper
- systems security0 papers
- adversarial robustness0 papers
Paper list
From the lab homepage.
20265 papers
- SaTMLBeyond the TESSERACT: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers (opens in a new tab)
- AAAI (T1)DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
- EuroS&PKnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
- USENIX Security (T1)SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
- ToSEMUnraveling the Key of Machine Learning-based Android Malware Detection
20253 papers
- TOPSIntriguing properties of adversarial ML attacks in the problem space [Extended Version]
- SaTMLML-Based Behavioral Malware Detection Is Far From a Solved Problem
- RAIDThe Adaptive Arms Race: Redefining Robustness in AI Security
20247 papers
- ICRA (T1)Characterizing Physical Adversarial Attacks on Robot Motion Planners
- CPSIoTSecEmbedWatch: Fat Pointer Solution for Detecting Spatial Memory Errors in Embedded Systems
- RAIDHow to Train your Antivirus: RL-based Hardening through the Problem Space
- DLSPOn Adaptive Decision-Based Attacks and Defenses
- ARTMANThe Impact of Active Learning on Availability Data Poisoning for Android Malware Classifiers
- DLSPWendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL
- AISecWhen Adversarial Perturbations meet Concept Drift: an Exploratory Analysis on ML-NIDS
20237 papers
- SaTML“Real Attackers Don’t Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice
- IEEE Security & Privacy MagazineAre Machine Learning Models for Malware Detection Ready for Prime Time? (opens in a new tab)
- AISecDrift Forensics of Malware Classifiers
- DLSPIs It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors
- S&P (T1)Jigsaw Puzzle: Selective backdoor attack to subvert malware classifiers
- CCS (T1)Poster: RPAL-Recovering Malware Classifiers from Data Poisoning using Active Learning
- IEEE Security & Privacy MagazineTaking the Red Pill: Lessons Learned on Machine Learning for Computer Security (opens in a new tab)
Where alumni went4 listed, 100% to industry
Industry 2Academia 0
Most often to
- Apple
- Meta
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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- The lab homepage
- Members
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Tiers are venue classifications, not lab rankings.