Shae McFaddenSM

ShaeMcFadden

Drag to turn the city. Each tower is a paper; select one to open it.

PhD StudentDept. of Informatics
King's College London

About

Shae McFadden

Currently a Ph.D. candidate, supervised by Dr. Fabio Pierazzi, at King's College London, investigating the applications of deep reinforcement learning to cybersecurity.

A passion for the cross-section between artificial intelligence and cybersecurity resulted in collaborations with the Systems Security Research Lab (S2Lab) at UCL and the AI for Cyber Defence Research Centre (AICD) at The Alan Turing Institute. Graduated from King’s College London with a first-class B.Sc. (Hons) in Computer Science specialising in Artificial Intelligence. During undergraduate studies, was awarded the Layton Research Award, the Alan Fairbourn Memorial Prize, and the Associateship of King’s College.

Education

  • PhD CandidateKing's College London, United Kingdom, 2023 - Present
  • BSc (Hons) Computer Science (Artificial Intelligence)King's College London, United Kingdom, 2020 - 2023

Experience

  • Visiting CollaboratorArtificial Intelligence for Cyber Defence (AICD) Research Centre, The Alan Turing Institute, 2023 - Present
  • Visiting ResearcherSystems Security Lab (S2Lab), University College London, 2024 - Present
  • King's Undergraduate Research FellowKing's College London, 2022 - 2022

Artificial Intelligence

  • Deep reinforcement learning applications for Cybersecurity.
  • Supervised learning for malware classification.

Cybersecurity

  • Malware classification
  • Vulnerability discovery.
  • Adversarial machine learning.
  • Autonomous cyber defence.

Selected publications

Every paper has its own page, explained in the paper's own words with diagrams and the results it reports. See all publications.

  • 2026 arXiv

    REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

    Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia, Alexander Herzog, Myles Foley, Chris Hicks, Lorenzo Cavallaro, Fabio Pierazzi

    arXiv preprint (cs.LG)

  • 2026 USENIX Sec

    SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

    Shae McFadden, Myles Foley, Elizabeth Bates, Ilias Tsingenopoulos, Sanyam Vyas, Vasilios Mavroudis, Chris Hicks, Fabio Pierazzi

    35th USENIX Security Symposium (USENIX Security 26)

  • 2026 TMLR

    Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points

    Dan Ristea, Shae McFadden, Ezzeldin Shereen, Madeleine Dwyer, Sanyam Vyas, Chris Hicks, Vasilios Mavroudis

    Transactions on Machine Learning Research

  • 2026 AAMAS Workshop

    Building Better Environments for Autonomous Cyber Defence

    Chris Hicks, Elizabeth Bates, Shae McFadden, Isaac Symes Thompson, Myles Foley, Ed Chapman,

    Workshop on AI for Critical National Infrastructure (AI4CNI) at AAMAS

  • 2026 AAAI

    DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift

    Shae McFadden, Myles Foley, Mario D'Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, Fabio Pierazzi

    Proc. of the AAAI Conference on Artificial Intelligence

  • 2026 TMLR

    One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image

    Ezzeldin Shereen, Dan Ristea, Shae McFadden, Burak Hasircioglu, Vasilios Mavroudis, Chris Hicks

    Transactions on Machine Learning Research

  • 2024 ACSAC Workshop

    The Impact of Active Learning on Availability Data Poisoning for Android Malware Classifiers

    Shae McFadden, Zeliang Kan, Lorenzo Cavallaro, Fabio Pierazzi

    Proc. of the Annual Computer Security Applications Conference Workshops (ACSAC Workshops)

  • 2024 S&P Workshop

    Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL

    Shae McFadden, Marcello Maugeri, Chris Hicks, Vasilis Mavroudis, Fabio Pierazzi

    Proc. of the IEEE Workshop on Deep Learning Security and Privacy (DLSP)

  • 2024 arXiv

    TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)

    Zeliang Kan, Shae McFadden, Daniel Arp, Feargus Pendlebury, Roberto Jordaney, Johannes Kinder, Fabio Pierazzi, Lorenzo Cavallaro

    arXiv preprint (cs.LG)

  • 2023 CCS

    Poster: RPAL-Recovering Malware Classifiers from Data Poisoning using Active Learning

    Shae McFadden, Zeliang Kan, Lorenzo Cavallaro, Fabio Pierazzi

    Proc. of ACM Conference on Computer and Communications Security (CCS)