Shae McFadden

Dept. of Informatics

King's College London

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.

News

Nov 01, 2024	Tesseract has been selected as a finalist in the ACSAC Cybersecurity Artifact Competition.
Oct 25, 2024	Our passive recovery paper has been accepted to the ARTMAN workshop co-located with ACSAC.
May 14, 2024	I will be attending IEEE S&P to present our paper in DLSP.

Selected Publications

ACSAC Competition

Impact of "TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time"

Shae McFadden, Zeliang Kan, Daniel Arp, Feargus Pendlebury, Roberto Jordaney, and 3 more authors

2024

Bib PDF

@misc{McFadden2024tesseract,
  title = {Impact of "{TESSERACT}: Eliminating Experimental Bias in Malware Classification across Space and Time"},
  author = {McFadden, Shae and Kan, Zeliang and Arp, Daniel and Pendlebury, Feargus and Jordaney, Roberto and Kinder, Johannes and Pierazzi, Fabio and Cavallaro, Lorenzo},
  booktitle = {ACSAC Cybersecurity Artifact Competition},
  year = {2024},
}

ARTMAN

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

Shae McFadden, Zeliang Kan, Lorenzo Cavallaro, and Fabio Pierazzi

In Proc. of the Annual Computer Security Applications Conference Workshops (ACSAC Workshops) , 2024

Bib PDF Code

@inproceedings{mcfadden2024recovery,
  title = {The Impact of Active Learning on Availability Data Poisoning for Android Malware Classifiers},
  author = {McFadden, Shae and Kan, Zeliang and Cavallaro, Lorenzo and Pierazzi, Fabio},
  booktitle = {Proc. of the Annual Computer Security Applications Conference Workshops ({ACSAC Workshops})},
  year = {2024},
}

DLSP

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

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

In Proc. of the IEEE Workshop on Deep Learning Security and Privacy (DLSP) , 2024

Bib PDF Code

@inproceedings{mcfadden2024wendigo,
  title = {Wendigo: Deep Reinforcement Learning for Denial-of-Service Query Discovery in GraphQL},
  author = {McFadden, Shae and Maugeri, Marcello and Hicks, Chris and Mavroudis, Vasilis and Pierazzi, Fabio},
  booktitle = {Proc. of the {IEEE} Workshop on Deep Learning Security and Privacy ({DLSP})},
  year = {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, and 3 more authors

2024

Bib PDF

@misc{kan2024tesseract,
  title = {TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)},
  author = {Kan, Zeliang and McFadden, Shae and Arp, Daniel and Pendlebury, Feargus and Jordaney, Roberto and Kinder, Johannes and Pierazzi, Fabio and Cavallaro, Lorenzo},
  year = {2024},
  eprint = {2402.01359},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
}

News

Selected Publications

Profiles