3 papers
cs.CR2025
Adversarially Robust Assembly Language Model for Packed Executables Detection
Shijia Li, Jiang Ming, Lanqing Liu +3
Detecting packed executables is a critical component of large-scale malware analysis and antivirus engine workflows, as it identifies samples that warrant computationally intensive…
cs.CR2025
Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model
Side Liu, Jiang Ming, Guodong Zhou +3
Malicious PDF files have emerged as a persistent threat and become a popular attack vector in web-based attacks. While machine learning-based PDF malware classifiers have shown pro…
cs.CR2021
App's Auto-Login Function Security Testing via Android OS-Level Virtualization
Wenna Song, Jiang Ming, Lin Jiang +5
Limited by the small keyboard, most mobile apps support the automatic login feature for better user experience. Therefore, users avoid the inconvenience of retyping their ID and pa…