Feargus Pendlebury

According to our database, Feargus Pendlebury authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Pitfalls in Machine Learning for Computer Security.

Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck

Commun. ACM, November, 2024

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

CoRR, 2024

2023

Are Machine Learning Models for Malware Detection Ready for Prime Time?

Lorenzo Cavallaro, Johannes Kinder, Feargus Pendlebury, Fabio Pierazzi

IEEE Secur. Priv., 2023

Lessons Learned on Machine Learning for Computer Security.

Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck

IEEE Secur. Priv., 2023

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers.

Limin Yang, Zhi Chen, Jacopo Cortellazzi, Feargus Pendlebury, Kevin Tu, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang

Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors.

Zhi Chen, Zhenning Zhang, Zeliang Kan, Limin Yang, Jacopo Cortellazzi, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang

Proceedings of the 2023 IEEE Security and Privacy Workshops (SPW), 2023

2022

Dos and Don'ts of Machine Learning in Computer Security.

Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, Konrad Rieck

Proceedings of the 31st USENIX Security Symposium, 2022

Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift.

Federico Barbero, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro

Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

2021

Universal Adversarial Perturbations for Malware.

Raphael Labaca Castro, Luis Muñoz-González, Feargus Pendlebury, Gabi Dreo Rodosek, Fabio Pierazzi, Lorenzo Cavallaro

CoRR, 2021

Investigating Labelless Drift Adaptation for Malware Detection.

Zeliang Kan, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro

Proceedings of the AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, 2021

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection.

Giuseppina Andresini, Feargus Pendlebury, Fabio Pierazzi, Corrado Loglisci, Annalisa Appice, Lorenzo Cavallaro

Proceedings of the AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, 2021

2020

Transcending Transcend: Revisiting Malware Classification with Conformal Evaluation.

Federico Barbero, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro

CoRR, 2020

Intriguing Properties of Adversarial ML Attacks in the Problem Space.

Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, Lorenzo Cavallaro

Proceedings of the 2020 IEEE Symposium on Security and Privacy, 2020

2019

TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time.

Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, Lorenzo Cavallaro

Proceedings of the 28th USENIX Security Symposium, 2019

2018

Enabling Fair ML Evaluations for Security.

Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney, Johannes Kinder, Lorenzo Cavallaro

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018