A Forensic Analysis of Android Malware -- How is Malware Written and How it Could Be Detected?, 2014 IEEE 38th Annual Computer Software and Applications Conference, pp.384-393, 2014. ,
DOI : 10.1109/COMPSAC.2014.61
Catch Me If You Can: Evaluating Android Anti-Malware Against Transformation Attacks, IEEE Transactions on Information Forensics and Security, vol.9, issue.1, pp.99-108, 2014. ,
DOI : 10.1109/TIFS.2013.2290431
Hiding Privacy Leaks in Android Applications Using Low-Attention Raising Covert Channels, 2013 International Conference on Availability, Reliability and Security, pp.701-710, 2013. ,
DOI : 10.1109/ARES.2013.92
URL : https://hal.archives-ouvertes.fr/hal-00857896
Steganography in Modern Smartphones and Mitigation Techniques, IEEE Communications Surveys & Tutorials, vol.17, issue.1, pp.334-357, 2014. ,
DOI : 10.1109/COMST.2014.2350994
Information Hiding as a Challenge for Malware Detection, IEEE Security & Privacy, vol.13, issue.2, pp.89-93, 2015. ,
DOI : 10.1109/MSP.2015.33
McAfee labs threat report, 2014. ,
Mobile Malware Detection Based on Energy Fingerprints ??? A Dead End?, Research in Attacks, Intrusions, and Defenses, pp.348-368, 2013. ,
DOI : 10.1007/978-3-642-41284-4_18
Accurate online power estimation and automatic battery behavior based power model generation for smartphones, Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, CODES/ISSS '10, pp.105-114, 2010. ,
DOI : 10.1145/1878961.1878982
The energy impact of security mechanisms in modern mobile devices, Network Security, vol.2012, issue.2, pp.11-14, 2012. ,
DOI : 10.1016/S1353-4858(12)70015-6
On energy-based profiling of malware in Android, 2014 International Conference on High Performance Computing & Simulation (HPCS), pp.535-542, 2014. ,
DOI : 10.1109/HPCSim.2014.6903732
Analysis of the communication between colluding applications on modern smartphones, Proceedings of the 28th Annual Computer Security Applications Conference on, ACSAC '12, pp.51-60, 2012. ,
DOI : 10.1145/2420950.2420958
Measuring and estimating power consumption in Android to support energy-based intrusion detection, Journal of Computer Security, vol.23, issue.5, pp.611-637, 2015. ,
DOI : 10.3233/JCS-150530
Neural Networks, A comprehensive foundation, 1999. ,
Classification and Regression Trees, 1984. ,
Behavioral detection of malware on mobile handsets, Proceeding of the 6th international conference on Mobile systems, applications, and services, MobiSys '08, pp.225-238, 2008. ,
DOI : 10.1145/1378600.1378626
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey, Information Security Technical Report, vol.14, issue.1, pp.16-29, 2009. ,
DOI : 10.1016/j.istr.2009.03.003
???Andromaly???: a behavioral malware detection framework for android devices, Journal of Intelligent Information Systems, vol.3597, issue.4, pp.161-190, 2012. ,
DOI : 10.1007/s10844-010-0148-x
VirusMeter: Preventing Your Cellphone from Spies, Recent Advances in Intrusion Detection, pp.244-264, 2009. ,
DOI : 10.1007/978-3-642-04342-0_13
Battery-based intrusion detection, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04., pp.2250-2255, 2004. ,
DOI : 10.1109/GLOCOM.2004.1378409
Towards an Intrusion Detection System for Battery Exhaustion Attacks on Mobile Computing Devices, Third IEEE International Conference on Pervasive Computing and Communications Workshops, pp.141-145, 2005. ,
DOI : 10.1109/PERCOMW.2005.86
Detecting energy-greedy anomalies and mobile malware variants, Proceeding of the 6th international conference on Mobile systems, applications, and services, MobiSys '08, pp.239-252, 2008. ,
DOI : 10.1145/1378600.1378627
Location based power analysis to detect malicious code in smartphones, Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices, SPSM '11, pp.27-32, 2011. ,
DOI : 10.1145/2046614.2046620
Power Based Malicious Code Detection Techniques for Smartphones, 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp.142-149, 2013. ,
DOI : 10.1109/TrustCom.2013.22
Time and Location Power Based Malicious Code Detection Techniques for Smartphones, 2014 IEEE 13th International Symposium on Network Computing and Applications, pp.261-268, 2014. ,
DOI : 10.1109/NCA.2014.45
Towards energy-aware intrusion detection systems on mobile devices, 2013 International Conference on High Performance Computing & Simulation (HPCS), pp.289-296, 2013. ,
DOI : 10.1109/HPCSim.2013.6641428
Battery-Sensing Intrusion Protection System, 2006 IEEE Information Assurance Workshop, pp.176-183, 2006. ,
DOI : 10.1109/IAW.2006.1652093
Battery Polling and Trace Determination for Bluetooth Attack Detection in Mobile Devices, 2007 IEEE SMC Information Assurance and Security Workshop, pp.135-142, 2007. ,
DOI : 10.1109/IAW.2007.381925
What is Green Security?, 2011 7th International Conference on Information Assurance and Security (IAS), pp.366-371, 2011. ,
DOI : 10.1109/ISIAS.2011.6122781
Preparing for malware that uses covert communication channels: The case of Tor-based Android malware, Int. Conf. Information Security and Digital Forensics, pp.85-96, 2014. ,
AndroSimilar, Proceedings of the 6th International Conference on Security of Information and Networks, SIN '13, pp.152-159, 2013. ,
DOI : 10.1145/2523514.2523539
Detection and Identification of Android Malware Based on Information Flow Monitoring, 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing, pp.1-4, 2015. ,
DOI : 10.1109/CSCloud.2015.27
URL : https://hal.archives-ouvertes.fr/hal-01191595
Soundcomber: A stealthy and context-aware sound trojan for smartphones, NDSS, pp.17-33, 2011. ,
A survey on energy-aware security mechanisms, Pervasive and Mobile Computing, vol.24, pp.77-90, 2015. ,
DOI : 10.1016/j.pmcj.2015.05.005
Breaking and fixing the Android Launching Flow, Computers & Security, vol.39, pp.104-115, 2013. ,
DOI : 10.1016/j.cose.2013.03.009
An Empirical Evaluation of the Android Security Framework, Security and Privacy Protection in Information Processing Systems, ser. IFIP Advances in Information and Communication Technology, pp.176-189, 2013. ,
DOI : 10.1007/978-3-642-21599-5_7
URL : https://hal.archives-ouvertes.fr/hal-01463826
EnTrack, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '15, pp.191-202, 2015. ,
DOI : 10.1145/2750858.2807531
The Elements of Statistical Learning, 2009. ,
Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems, Journal of Optimization Theory and Applications, vol.14, issue.2, pp.403-439, 2002. ,
DOI : 10.1023/A:1013662124879
Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results, Journal of Optimization Theory and Applications, vol.2, issue.6, pp.380-416, 2013. ,
DOI : 10.1007/s10957-012-0118-2
Approximate dynamic programming for stochastic N-stage optimization with application to optimal consumption under uncertainty, Computational Optimization and Applications, vol.112, issue.6, pp.31-85, 2014. ,
DOI : 10.1007/s10589-013-9614-z
Enabling cooperation of consumer devices through peer-to-peer overlays, IEEE Transactions on Consumer Electronics, vol.55, issue.2, pp.414-421, 2009. ,
DOI : 10.1109/TCE.2009.5174402
Nonlinear predictive control for the management of container flows in maritime intermodal terminals, 2008 47th IEEE Conference on Decision and Control, pp.1423-1431, 2013. ,
DOI : 10.1109/CDC.2008.4739146
Multilayer feedforward networks are universal approximators, Multilayer feedforward networks are universal approximators, pp.359-366, 1989. ,
DOI : 10.1016/0893-6080(89)90020-8
Universal approximation bounds for superpositions of a sigmoidal function, IEEE Transactions on Information Theory, vol.39, issue.3, pp.930-945, 1993. ,
DOI : 10.1109/18.256500
Statistical Learning from a Regression Perspective, 2008. ,
DOI : 10.1007/978-3-319-44048-4
An analysis based on Fdiscrepancy for sampling in regression tree learning, Proc. Int. Joint Conf. on Neural Networks, pp.1115-1121, 2014. ,
An Algorithm for Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963. ,
DOI : 10.1137/0111030
The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, 1994. ,
Regression Analysis -Theory, Methods, and Applications, 2011. ,
Elements of Information Theory, 1991. ,