arXiv Machine Learning
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 31
REPLICANT: Learning Policies for Evading and Hardening Malware Detectors
The paper introduces Replicant, a deep reinforcement learning framework that learns to evade malware detectors under a strict label‑only black‑box threat model. Replicant generates reusable policies for modifying malware samples and deciding when to query the target, and it transfers across different samples, detectors, and feature spaces. In experiments on seven Android malware detectors and three feature spaces, Replicant achieves a mean attack success rate of 78.8%, outperforming state‑of‑the‑art methods by 20.9%–39.2% and providing a stronger signal for adversarial training to harden detectors.
arXiv Machine Learning
Jul 13
Regression-aware Continual Learning for Android Malware Detection
arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.
arXiv Machine Learning
Jun 8
Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
arXiv:2604. 23025v2 Announce Type: replace-cross Abstract: Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness.
arXiv AI
Jun 3
High-Precision APT Malware Attribution with Out-of-Scope Resilience
arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.
arXiv Machine Learning
Aug 5
ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection
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.
arXiv Machine Learning
Jun 26
DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors
arXiv:2606. 26707v1 Announce Type: cross Abstract: Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors.