Lorenzo Cavallaro
Professor of Computer Science
Leads the Systems Security Research Lab, studying adversarial machine learning and concept drift in ML-based malware detection.
Lorenzo Cavallaro's lab at UCL works on trustworthy & safe ML and recognition, detection & segmentation. 32 papers since 2023, 4 at top venues, mostly USENIX Security and ICML.
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 13%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 0 · collecting
- 8 papers2025 · T1 1
- 7 papers2024 · T1 2
- 10 papers2023 · T1 1
T1 venuesOtherVenues: USENIX Security, ICML, S&P
Research focuspaper titles that use each topic's words
- malware14 papers
- Recognition, detection & segmentation7 papers
- Trustworthy & safe ML1 paper
- systems security0 papers
- adversarial machine0 papers
Paper list
From the lab homepage.
20267 papers
- SaTMLBeyond the TESSERACT: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers (opens in a new tab)
- Network and Distributed System Security (NDSS) SymposiumBINALIGNER: Aligning Binary Code for Cross-Compilation Environment Diffing (opens in a new tab)
- IEEE Transactions on Information Forensics and SecurityDefending against Adversarial Malware Attacks on ML-based Android Malware Detection Methods (opens in a new tab)
- Proceedings of the ACM on Software EngineeringIs “Knowing It’s Malicious” Enough? Evaluating LLMs for Fine-Grained Malware Behavior Auditing
- EuroS&PKnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs (opens in a new tab)
- IEEE Transactions on Software EngineeringTIF: Learning Temporal Invariance in Android Malware Detectors (opens in a new tab)
- ACM Transactions on Software Engineering and MethodologyUnraveling the Key of Machine Learning-based Android Malware Detection (opens in a new tab)
20258 papers
- USENIX Security (T1)Expert insights into advanced persistent threats: analysis, attribution, and challenges (opens in a new tab)
- TOPSIntriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version] (opens in a new tab)
- Security and Privacy Workshops-SPWLAMD: Context-driven Android Malware Detection and Classification with LLMs (opens in a new tab)
- SaTMLML-Based Behavioral Malware Detection Is Far From a Solved Problem (opens in a new tab)
- ISSTA Companion '25: ACM SIGSOFT International Symposium on Software Testing and AnalysisOn Benchmarking Code LLMs for Android Malware Analysis (opens in a new tab)
- IEEE European Symposium on Security and PrivacyOn the Lack of Robustness of Binary Function Similarity Systems (opens in a new tab)
- RAIDThe Adaptive Arms Race: Redefining Robustness in AI Security (opens in a new tab)
- Annual Computer Security Applications Conference WorkshopsThe Impact of Active Learning on Availability Data Poisoning for Android Malware Classifiers (opens in a new tab)
20247 papers
- RAIDADAPT it! Automating APT Campaign and Group Attribution by Leveraging and Linking Heterogeneous Files (opens in a new tab)
- USENIX Security (T1)ChainReactor: Automated Privilege Escalation Chain Discovery via AI Planning (opens in a new tab)
- Workshop on CPS&IoT Security and PrivacyEmbedWatch: Fat Pointer Solution for Detecting Spatial Memory Errors in Embedded Systems (opens in a new tab)
- ICML (T1)Exploiting Code Symmetries for Learning Program Semantics (opens in a new tab)
- RAID '24: The International Symposium on Research in Attacks, Intrusions and DefensesHow to Train your Antivirus: RL-based Hardening through the Problem Space (opens in a new tab)
- ACM Computing SurveysIdentifying Authorship in Malicious Binaries: Features, Challenges & Datasets (opens in a new tab)
- Communications of the ACMPitfalls in Machine Learning for Computer Security (opens in a new tab)
202310 papers
- IEEE Security and Privacy MagazineAre Machine Learning Models for Malware Detection Ready for Prime Time? (opens in a new tab)
- MM '23: The ACM International Conference on MultimediaCross-Modal and Multi-Attribute Face Recognition: A Benchmark
- MM '23: The ACM International Conference on MultimediaDFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues (opens in a new tab)
- CCS '23: ACM SIGSAC Conference on Computer and Communications SecurityDrift Forensics of Malware Classifiers (opens in a new tab)
- SPWIs It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors (opens in a new tab)
- S&P (T1)Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers (opens in a new tab)
- IEEE Security & PrivacyLessons Learned on Machine Learning for Computer Security (opens in a new tab)
- MM '23: The ACM International Conference on MultimediaLocate and Verify: A Two-Stream Network for Improved Deepfake Detection (opens in a new tab)
- CCS '23: ACM SIGSAC Conference on Computer and Communications SecurityPoster: RPAL-Recovering Malware Classifiers from Data Poisoning using Active Learning (opens in a new tab)
- WOOTROPfuscator: Robust Obfuscation with ROP (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
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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