ABSTRACT
As smartphones 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 malware detection. The comparison of modeling results demonstrates that the deep learning technique is especially suitable for Android malware detection and can achieve a high level of 96% accuracy with real-world Android application sets.
- Y. Bengio. Learning deep architectures for ai. Foundations and trends in Machine Learning, 2(1):1--127, 2009. Google ScholarDigital Library
- W. Enck, P. Gilbert, B.-G. Chun, L. P. Cox, J. Jung, P. McDaniel, and A. Sheth. Taintdroid: An information-flow tracking system for realtime privacy monitoring on smartphones. In OSDI'10, volume 10, pages 1--6, 2010. Google ScholarDigital Library
- Y. Zhou and X. Jiang. Dissecting android malware: characterization and evolution. In IEEE S&P'12, pages 95--109, 2012. Google ScholarDigital Library
Index Terms
- Droid-Sec: deep learning in android malware detection
Recommendations
Droid-Sec: deep learning in android malware detection
SIGCOMM'14As smartphones 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 ...
Droid Analytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware
TRUSTCOM '13: Proceedings of the 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and CommunicationsSmartphones and mobile devices are rapidly becoming indispensable devices for many users. Unfortunately, they also become fertile grounds for hackers to deploy malware. There is an urgent need to have a "security analytic & forensic system" which can ...
Permission based malware detection in android devices
SCA '18: Proceedings of the 3rd International Conference on Smart City ApplicationsThe mobile operation system Android is one of the most OS's used in the entire world, which make it the target of many malware projects and the mission of detecting those malware applications is getting harder over time due to evaluation and development ...
Comments