arXiv:2608. 03250v1 Announce Type: cross Abstract: The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications.
By Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil, Ahmed Ann Noor Ryen, Arman Hossain, Faisal Bin Ashraf, Muhammad Iqbal Hossain
arXiv:2503.11841v2 Announce Type: replace-cross
Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as truste...
By Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam, Yufei Han, Simone Aonzo
arXiv:2605. 09028v3 Announce Type: replace Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another.
By Md Rafid Islam
arXiv:2606. 16072v1 Announce Type: cross Abstract: Compared with binaries and decompiled code, malware source code more directly reflects the attackers' original intent.
By Bojing Li, Duo Zhong, Prajna Bhandary, Raguvir S, Charles Maxa, Robert J Joyce, Charles Nicholas
arXiv:2512. 20872v2 Announce Type: replace-cross Abstract: Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures.
By Jakir Hossain, Jue Guo, Gurvinder Singh, Lukasz Ziarek, Ahmet Erdem Sar{\i}y\"uce
The study evaluates pseudo‑labeling for semi‑supervised learning on Android malware attribution using six classifiers. Results show that the benefit of SSL varies strongly by classifier: SVM gains the most, LightGBM improves modestly, and Random Forest can be harmed at low label ratios. The approach particularly helps hard‑to‑classify families and achieves near‑optimal performance with about 800 labeled samples.
By Md Rafid Islam, Zahid Hasan, Hafiz Abdur Rahman