Towards Light-Weight Deep Learning Based Malware Detection. - Zeliang Kan; Haoyu Wang 001; Guoai Xu; Yao Guo 001; Xiangqun Chen (COMPSAC (1), 2018)

Deobfuscating Android native binary code. - Zeliang Kan; Haoyu Wang 001; Lei Wu 012; Yao Guo 001; Guoai Xu (ICSE (Companion Volume), 2019)

Automated Deobfuscation of Android Native Binary Code. - Zeliang Kan; Haoyu Wang 001; Lei Wu 012; Yao Guo 001; Daniel Xiapu Luo (CoRR, 2019)

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

Drift Forensics of Malware Classifiers. - Theo Chow; Zeliang Kan; Lorenz Linhardt; Lorenzo Cavallaro; Daniel Arp; Fabio Pierazzi (AISec@CCS, 2023)

Poster: RPAL-Recovering Malware Classifiers from Data Poisoning using Active Learning. - Shae McFadden; Zeliang Kan; Lorenzo Cavallaro; Fabio Pierazzi (CCS, 2023)

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)

The Impact of Active Learning on Availability Data Poisoning for Android Malware Classifiers. - Shae McFadden; Zeliang Kan; Lorenzo Cavallaro; Fabio Pierazzi (ACSAC Workshops, 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)

Breaking Out from the TESSERACT: Reassessing ML-based Malware Detection under Spatio-Temporal Drift. - Theo Chow; Mario D'Onghia; Lorenz Linhardt; Zeliang Kan; Daniel Arp; Lorenzo Cavallaro; Fabio Pierazzi (CoRR, 2025)