AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
In the second phase, the malicious applications are executed in a virtual emulator to reduce the number of false positives. In the first phase, a lightweight deep transfer learning approach is used to classify Android applications into benign and malicious. In the end, a three-phase model is proposed to efficiently identify and characterize Android malware. ![]() A comparative analysis is presented between this article and similar recent survey articles to fill the existing research gaps. To overcome the research gaps, this paper provides a broad review of current Android security concerns, security implementation enhancements, significant malware detected during 2017–2021, and stealth procedures used by the malware developers along with the current Android malware detection techniques. However, to examine Android security, with a specific focus on malware development, investigation of malware prevention techniques and already known malware detection techniques needs a broad inclusion. Most of the researchers have focused on Android system security. ![]() It is hence desirable that security researchers and experts come up with novel and more efficient methods to analyze existing and zero-day Android malware. Malware developers are also able to evade the detection methods, reducing the efficiency of malware detection techniques. The popularity and open-source nature of Android devices have resulted in a dramatic growth of Android malware.
0 Comments
Read More
Leave a Reply. |