Using Deep Neural Network for Android Malware Detection
Not on the map. Why?
Abstract (arXiv)
The pervasiveness of the Android operating system, with the availability of applications almost for everything, is readily accessible in the official Google play store or a dozen alternative third-party markets. Additionally, the vital role of smartphones in modern life leads to store significant information on devices, not only personal information but also corporate information, which attract malware developers to develop applications that can infiltrate user's devices to steal information and perform harmful tasks. This accompanied with the limitation of currently defenses techniques such as ineffective screening in Google play store, weak or no screening in third-party markets. Antiviruses software that still relies on a signature-based database that is effective only in identifying known malware. To contrive with malicious applications that are increased in volume and sophistication, we propose an Android malware detection system that applies deep learning technique to face the threats of Android malware. Extensive experiments on a real-world dataset contain benign and malicious applications uncovered that the proposed system reaches an accuracy of 95.31%.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 16 Jan 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1.
1 paper.