Shunzhe Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shunzhe Zhao has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Guangquan Xu
Tianjin University
2 shared publications - Haobo Zhang
Shihezi University, Shihezi
2 shared publications - Hongpeng Bai
Tianjin University
2 shared publications - Lingyue Li
Tianjin University
2 shared publications - Yao Zhang
Tianjin University
2 shared publications - Yude Bai
Tiangong University
2 shared publications
Latest Publications
- 29 October 2026
- Published by Springer Nature
- p. 33-60
AndroTruth: A Reliable Benchmark Android Malware Dataset Derived from Technical Expert Reports
- 29 October 2026
- Published by Springer Nature
- p. 33-60
AndroTruth: A Reliable Benchmark Android Malware Dataset Derived from Technical Expert Reports
- Open Access
ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research
The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial cybersecurity. However, mainstream datasets widely used in academia and industry (e.g., Drebin) exhibit significant limitations: on one hand, their heavy reliance on VirusTotal's multi-engine aggregation results introduces substantial label noise; on the other hand, outdated samples reduce their temporal relevance. Moreover, automated labeling tools (e.g., AVClass2) suffer from suboptimal aggregation strategies, further compounding labeling errors and propagating inaccuracies throughout the research community.
Open AccessThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research
The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial cybersecurity. However, mainstream datasets widely used in academia and industry (e.g., Drebin) exhibit significant limitations: on one hand, their heavy reliance on VirusTotal's multi-engine aggregation results introduces substantial label noise; on the other hand, outdated samples reduce their temporal relevance. Moreover, automated labeling tools (e.g., AVClass2) suffer from suboptimal aggregation strategies, further compounding labeling errors and propagating inaccuracies throughout the research community.