Mobile Phones as Computing Devices: The Viruses are Coming!
IEEE Pervasive Computing
SmartSiren: virus detection and alert for smartphones
Proceedings of the 5th international conference on Mobile systems, applications and services
Static analysis of executables to detect malicious patterns
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Mining specifications of malicious behavior
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Intrusion detection using sequences of system calls
Journal of Computer Security
Mobile Device Profiling and Intrusion Detection Using Smart Batteries
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Behavioral detection of malware on mobile handsets
Proceedings of the 6th international conference on Mobile systems, applications, and services
Detecting energy-greedy anomalies and mobile malware variants
Proceedings of the 6th international conference on Mobile systems, applications, and services
Learning and Classification of Malware Behavior
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Monitoring smartphones for anomaly detection
Mobile Networks and Applications
Understanding Android Security
IEEE Security and Privacy
Information Security Tech. Report
Towards Formal Analysis of the Permission-Based Security Model for Android
ICWMC '09 Proceedings of the 2009 Fifth International Conference on Wireless and Mobile Communications
Semantically Rich Application-Centric Security in Android
ACSAC '09 Proceedings of the 2009 Annual Computer Security Applications Conference
Google Android: A Comprehensive Security Assessment
IEEE Security and Privacy
Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method
Journal of Systems and Software
Static analysis of executables for collaborative malware detection on android
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Malware Detection on Mobile Devices Using Distributed Machine Learning
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Formal Model to Analyze the Permission Authorization and Enforcement in the Android Framework
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
A Small But Non-negligible Flaw in the Android Permission Scheme
POLICY '10 Proceedings of the 2010 IEEE International Symposium on Policies for Distributed Systems and Networks
Paranoid Android: versatile protection for smartphones
Proceedings of the 26th Annual Computer Security Applications Conference
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Detection of malicious applications on Android OS
IWCF'10 Proceedings of the 4th international conference on Computational forensics
A study of android application security
SEC'11 Proceedings of the 20th USENIX conference on Security
A survey on automated dynamic malware-analysis techniques and tools
ACM Computing Surveys (CSUR)
"Andromaly": a behavioral malware detection framework for android devices
Journal of Intelligent Information Systems
Defending users against smartphone apps: techniques and future directions
ICISS'11 Proceedings of the 7th international conference on Information Systems Security
Modular anomaly detection for smartphone ad hoc communication
NordSec'11 Proceedings of the 16th Nordic conference on Information Security Technology for Applications
ProfileDroid: multi-layer profiling of android applications
Proceedings of the 18th annual international conference on Mobile computing and networking
Aurasium: practical policy enforcement for Android applications
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Dr. Android and Mr. Hide: fine-grained permissions in android applications
Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices
SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications
Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices
PScout: analyzing the Android permission specification
Proceedings of the 2012 ACM conference on Computer and communications security
Exposing security risks for commercial mobile devices
MMM-ACNS'12 Proceedings of the 6th international conference on Mathematical Methods, Models and Architectures for Computer Network Security: computer network security
MADAM: a multi-level anomaly detector for android malware
MMM-ACNS'12 Proceedings of the 6th international conference on Mathematical Methods, Models and Architectures for Computer Network Security: computer network security
Permission-based abnormal application detection for android
ICICS'12 Proceedings of the 14th international conference on Information and Communications Security
Sweetening android lemon markets: measuring and combating malware in application marketplaces
Proceedings of the third ACM conference on Data and application security and privacy
Proceedings of the third ACM conference on Data and application security and privacy
Towards an understanding of the impact of advertising on data leaks
International Journal of Security and Networks
Tap-Wave-Rub: lightweight malware prevention for smartphones using intuitive human gestures
Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks
Mobile-sandbox: having a deeper look into android applications
Proceedings of the 28th Annual ACM Symposium on Applied Computing
ADAM: an automatic and extensible platform to stress test android anti-virus systems
DIMVA'12 Proceedings of the 9th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
MeadDroid: detecting monetary theft attacks in android by DVM monitoring
ICISC'12 Proceedings of the 15th international conference on Information Security and Cryptology
Android provenance: diagnosing device disorders
TaPP'13 Proceedings of the 5th USENIX conference on Theory and Practice of Provenance
Android provenance: diagnosing device disorders
Proceedings of the 5th USENIX Workshop on the Theory and Practice of Provenance
DroidChameleon: evaluating Android anti-malware against transformation attacks
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
Real-time detection and prevention of android SMS permission abuses
Proceedings of the first international workshop on Security in embedded systems and smartphones
Proceedings of the Ninth Symposium on Usable Privacy and Security
Vetting undesirable behaviors in android apps with permission use analysis
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Is data clustering in adversarial settings secure?
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
PREC: practical root exploit containment for android devices
Proceedings of the 4th ACM conference on Data and application security and privacy
Detecting mobile malware threats to homeland security through static analysis
Journal of Network and Computer Applications
The company you keep: mobile malware infection rates and inexpensive risk indicators
Proceedings of the 23rd international conference on World wide web
Electronic Commerce Research
| Hi-index | 0.00 |
The sharp increase in the number of smartphones on the market, with the Android platform posed to becoming a market leader makes the need for malware analysis on this platform an urgent issue. In this paper we capitalize on earlier approaches for dynamic analysis of application behavior as a means for detecting malware in the Android platform. The detector is embedded in a overall framework for collection of traces from an unlimited number of real users based on crowdsourcing. Our framework has been demonstrated by analyzing the data collected in the central server using two types of data sets: those from artificial malware created for test purposes, and those from real malware found in the wild. The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware. This shows the potential for avoiding the spreading of a detected malware to a larger community.