Enabling Fair ML Evaluations for Security. - Feargus Pendlebury; Fabio Pierazzi; Roberto Jordaney; Johannes Kinder; Lorenzo Cavallaro (CCS, 2018)

TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time. - Feargus Pendlebury; Fabio Pierazzi; Roberto Jordaney; Johannes Kinder; Lorenzo Cavallaro (USENIX Security Symposium, 2019)

Intriguing Properties of Adversarial ML Attacks in the Problem Space. - Fabio Pierazzi; Feargus Pendlebury; Jacopo Cortellazzi; Lorenzo Cavallaro (SP, 2020)

Transcending Transcend: Revisiting Malware Classification with Conformal Evaluation. - Federico Barbero; Feargus Pendlebury; Fabio Pierazzi; Lorenzo Cavallaro (CoRR, 2020)

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection. - Giuseppina Andresini; Feargus Pendlebury; Fabio Pierazzi; Corrado Loglisci; Annalisa Appice; Lorenzo Cavallaro (AISec@CCS, 2021)

Investigating Labelless Drift Adaptation for Malware Detection. - Zeliang Kan; Feargus Pendlebury; Fabio Pierazzi; Lorenzo Cavallaro (AISec@CCS, 2021)

Universal Adversarial Perturbations for Malware. - Raphael Labaca Castro; Luis Muñoz-González; Feargus Pendlebury; Gabi Dreo Rodosek; Fabio Pierazzi; Lorenzo Cavallaro (CoRR, 2021)

Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift. - Federico Barbero; Feargus Pendlebury; Fabio Pierazzi; Lorenzo Cavallaro (SP, 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 (USENIX Security Symposium, 2022)

Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors. - Zhi Chen 028; Zhenning Zhang; Zeliang Kan; Limin Yang; Jacopo Cortellazzi; Feargus Pendlebury; Fabio Pierazzi; Lorenzo Cavallaro; Gang Wang 011 (SP (Workshops), 2023)

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers. - Limin Yang; Zhi Chen 028; Jacopo Cortellazzi; Feargus Pendlebury; Kevin Tu; Fabio Pierazzi; Lorenzo Cavallaro; Gang Wang 011 (SP, 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)

Are Machine Learning Models for Malware Detection Ready for Prime Time? - Lorenzo Cavallaro; Johannes Kinder; Feargus Pendlebury; Fabio Pierazzi (IEEE Secur. Priv., 2023)

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)

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, 2024)