L Chen, S Hou, Y Ye - Proceedings of the 33rd Annual Computer …, 2017 - dl.acm.org
With smart phones being indispensable in people's everyday life, Android malware has posed serious threats to their security, making its detection of utmost concern. To protect …
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection …
The increasing proliferation of Androidbased devices, which currently dominate the market with a staggering 72% global market share, has made them a prime target for attackers …
Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it …
Machine Learning-based malware detection is a promis-ing scalable method for identifying suspicious applica-tions. In particular, in today's mobile computing realm where thousands …
Machine learning-based solutions have been successfully employed for the automatic detection of malware on Android. However, machine learning models lack robustness to …
X Fu, H Cai - 2019 IEEE/ACM 41st International Conference on …, 2019 - ieeexplore.ieee.org
Classification using machine learning has been a major class of defense solutions against malware. Yet in the presence of a large and growing number of learning-based malware …
X Wang, L Zhang, K Zhao, X Ding, M Yu - Sensors, 2022 - mdpi.com
As Android is a popular a mobile operating system, Android malware is on the rise, which poses a great threat to user privacy and security. Considering the poor detection effects of …
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …