Shae McFadden

Dept. of Informatics

King's College London

I am currently undergoing my PhD at King’s College London under the supervision of Dr. Fabio Pierazzi. My primary research interest is in the cross section between artificial intelligence and cybersecurity. The focus of my thesis is on the application of deep reinforcement learning to cybersecurity, which at the moment has me looking at its application to black-box red team problems. Prior to starting my PhD, I conducted research on machine learning for malware classification in my undergraduate dissertation that won the Alan Fairbourn Memorial Prize and sparked my interest in AI and cybersecurity.

News

Oct 25, 2024	Our passive recovery 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

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