Journal
ACM SIGCOMM COMPUTER COMMUNICATION REVIEW
Volume 44, Issue 4, Pages 371-372Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2740070.2631434
Keywords
Android malware; deep learning; detection
Categories
Ask authors/readers for more resources
As smart:phones and mobile devices are rapidly becoming indispensable for many network users, mobile malware has become a serious threat in the network security and privacy. Especially on the popular Android platform, many malicious apps are hiding in a large number of normal apps, which makes the malware detection more challenging. In this paper, we propose a ML-based method that utilizes more than 200 features extracted from both static analysis and dynamic analysis of Android app for malv -are detection. The comparison of modeling results demonstrates that the deep learning technique is especially suitable for Android mahvare detection and can achieve a high level of 96% accuracy with real-world Android application sets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available