Anomalous system call detection
ACM Transactions on Information and System Security (TISSEC)
Proactive security for mobile messaging networks
WiSe '06 Proceedings of the 5th ACM workshop on Wireless security
Using Battery Constraints within Mobile Hosts to Improve Network Security
IEEE Security and Privacy
Monitoring smartphones for anomaly detection
Mobile Networks and Applications
On lightweight mobile phone application certification
Proceedings of the 16th ACM conference on Computer and communications security
Semantically Rich Application-Centric Security in Android
ACSAC '09 Proceedings of the 2009 Annual Computer Security Applications Conference
pBMDS: a behavior-based malware detection system for cellphone devices
Proceedings of the third ACM conference on Wireless network security
Proceedings of the 8th international conference on Mobile systems, applications, and services
Static analysis of executables for collaborative malware detection on android
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Crowdroid: behavior-based malware detection system for Android
Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices
"Andromaly": a behavioral malware detection framework for android devices
Journal of Intelligent Information Systems
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Quantifying and Classifying Covert Communications on Android
Mobile Networks and Applications
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Currently, in the smartphone market, Android is the platform with the highest share. Due to this popularity and also to its open source nature, Android-based smartphones are now an ideal target for attackers. Since the number of malware designed for Android devices is increasing fast, Android users are looking for security solutions aimed at preventing malicious actions from damaging their smartphones. In this paper, we describe MADAM, a Multi-level Anomaly Detector for Android Malware. MADAM concurrently monitors Android at the kernel-level and user-level to detect real malware infections using machine learning techniques to distinguish between standard behaviors and malicious ones. The first prototype of MADAM is able to detect several real malware found in the wild. The device usability is not affected by MADAM due to the low number of false positives generated after the learning phase.