恶意软件
Android(操作系统)
计算机科学
可扩展性
操作系统
隐病毒学
软件
嵌入式系统
作者
Uday Sai Kumar,Ashok Yadav,Vrijendra Singh
标识
DOI:10.1109/upcon56432.2022.9986470
摘要
Android is the most popular operating system for smartphones and tablets. With its popularity, Android mal ware has also grown dramatically. Many conventional malware detection techniques are now not sufficient, due to sophisticated detection avoidance strategies. According to ongoing research, one harmful Android software is released every 10 seconds. To counter these significant mal ware campaigns, scalable detection approaches require that can provide quick and accurate identification of mal ware apps. To overcome the above issues, we proposed a method to detect malware in Android applications by extracting features like activities, services, requested permissions, and intent filters from the manifest file. Furthermore, the androguard tool is used to disassemble the code and extract all suspicious API calls by reading the dex code. These extracted features are serialized in feather data format for efficient retrieval. After that, the XGBoost algorithm is used to detect the malware. The result of the proposed method gives 97% accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI