Background and Related Work

[1]  Barbara G. Ryder,et al.  Detection of Repackaged Android Malware with Code-Heterogeneity Features , 2020, IEEE Transactions on Dependable and Secure Computing.

[2]  Sunil K. Muttoo,et al.  CENDroid - A cluster-ensemble classifier for detecting malicious Android applications , 2019, Comput. Secur..

[3]  Ke Xu,et al.  DroidEvolver: Self-Evolving Android Malware Detection System , 2019, 2019 IEEE European Symposium on Security and Privacy (EuroS&P).

[4]  Eul Gyu Im,et al.  A Multimodal Deep Learning Method for Android Malware Detection Using Various Features , 2019, IEEE Transactions on Information Forensics and Security.

[5]  Wenke Lee,et al.  Improving Accuracy of Android Malware Detection with Lightweight Contextual Awareness , 2018, ACSAC.

[6]  Robert H. Deng,et al.  DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks , 2018, 2018 IEEE European Symposium on Security and Privacy (EuroS&P).

[7]  Kevin Jones,et al.  Malware classification using self organising feature maps and machine activity data , 2018, Comput. Secur..

[8]  Golden G. Richard,et al.  Toward a more dependable hybrid analysis of android malware using aspect-oriented programming , 2018, Comput. Secur..

[9]  Qinghua Zheng,et al.  Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis , 2018, IEEE Transactions on Information Forensics and Security.

[10]  Gianluca Dini,et al.  MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention , 2018, IEEE Transactions on Dependable and Secure Computing.

[11]  Muttukrishnan Rajarajan,et al.  PIndroid: A novel Android malware detection system using ensemble learning , 2017 .

[12]  Fabio Martinelli,et al.  BRIDEMAID: An Hybrid Tool for Accurate Detection of Android Malware , 2017, AsiaCCS.

[13]  Elisa Bertino,et al.  Android resource usage risk assessment using hidden Markov model and online learning , 2017, Comput. Secur..

[14]  Gianluca Stringhini,et al.  MaMaDroid , 2019, ACM Trans. Priv. Secur..

[15]  Zhenkai Liang,et al.  Monet: A User-Oriented Behavior-Based Malware Variants Detection System for Android , 2016, IEEE Transactions on Information Forensics and Security.

[16]  Abdelouahid Derhab,et al.  Cypider: building community-based cyber-defense infrastructure for android malware detection , 2016, ACSAC.

[17]  Saed Alrabaee,et al.  DySign: dynamic fingerprinting for the automatic detection of android malware , 2016, 2016 11th International Conference on Malicious and Unwanted Software (MALWARE).

[18]  Erik Derr,et al.  On Demystifying the Android Application Framework: Re-Visiting Android Permission Specification Analysis , 2016, USENIX Security Symposium.

[19]  Mourad Debbabi,et al.  Fingerprinting Android packaging: Generating DNAs for malware detection , 2016, Digit. Investig..

[20]  Minhui Xue,et al.  StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware , 2016, AsiaCCS.

[21]  Aziz Mohaisen,et al.  Andro-Dumpsys: Anti-malware system based on the similarity of malware creator and malware centric information , 2016, Comput. Secur..

[22]  Eric Medvet,et al.  Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection , 2016, IWSPA@CODASPY.

[23]  Golden G. Richard,et al.  AspectDroid: Android App Analysis System , 2016, CODASPY.

[24]  Ke Xu,et al.  ICCDetector: ICC-Based Malware Detection on Android , 2016, IEEE Transactions on Information Forensics and Security.

[25]  Golden G. Richard,et al.  OpSeq: Android Malware Fingerprinting , 2015, PPREW@ACSAC.

[26]  Eul Gyu Im,et al.  Structural information based malicious app similarity calculation and clustering , 2015, RACS.

[27]  Hao Chen,et al.  AnDarwin: Scalable Detection of Android Application Clones Based on Semantics , 2015, IEEE Transactions on Mobile Computing.

[28]  Vijay Laxmi,et al.  DRACO: DRoid analyst combo an android malware analysis framework , 2015, SIN.

[29]  Peng Wang,et al.  Finding Unknown Malice in 10 Seconds: Mass Vetting for New Threats at the Google-Play Scale , 2015, USENIX Security Symposium.

[30]  Heejo Lee,et al.  Screening smartphone applications using malware family signatures , 2015, Comput. Secur..

[31]  John C. S. Lui,et al.  DroidEagle: seamless detection of visually similar Android apps , 2015, WISEC.

[32]  Vijay Laxmi,et al.  AndroSimilar: Robust signature for detecting variants of Android malware , 2015, J. Inf. Secur. Appl..

[33]  Xuxian Jiang,et al.  Profiling user-trigger dependence for Android malware detection , 2015, Comput. Secur..

[34]  Muttukrishnan Rajarajan,et al.  Android Security: A Survey of Issues, Malware Penetration, and Defenses , 2015, IEEE Communications Surveys & Tutorials.

[35]  Xuxian Jiang,et al.  Design and implementation of an Android host-based intrusion prevention system , 2014, ACSAC.

[36]  Isil Dillig,et al.  Apposcopy: semantics-based detection of Android malware through static analysis , 2014, SIGSOFT FSE.

[37]  Nicolas Christin,et al.  A5: Automated Analysis of Adversarial Android Applications , 2014, SPSM@CCS.

[38]  Mu Zhang,et al.  Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs , 2014, CCS.

[39]  Yanick Fratantonio,et al.  ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors , 2014, 2014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS).

[40]  Jacques Klein,et al.  FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps , 2014, PLDI.

[41]  Juanru Li,et al.  APKLancet: tumor payload diagnosis and purification for android applications , 2014, AsiaCCS.

[42]  Peng Wang,et al.  AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction , 2014, ICSE.

[43]  Georgios Kambourakis,et al.  The best of both worlds: a framework for the synergistic operation of host and cloud anomaly-based IDS for smartphones , 2014, EuroSec '14.

[44]  Dennis G. Kafura,et al.  DroidBarrier: know what is executing on your android , 2014, CODASPY '14.

[45]  Landon P. Cox,et al.  TaintDroid , 2014 .

[46]  Juan E. Tapiador,et al.  Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families , 2014, Expert Syst. Appl..

[47]  Arun Lakhotia,et al.  DroidLegacy: Automated Familial Classification of Android Malware , 2014, PPREW'14.

[48]  Konrad Rieck,et al.  DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.

[49]  Eric Bodden,et al.  A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks , 2014, NDSS.

[50]  Vijay Laxmi,et al.  AndroSimilar: robust statistical feature signature for Android malware detection , 2013, SIN.

[51]  Yuan Zhang,et al.  Vetting undesirable behaviors in android apps with permission use analysis , 2013, CCS.

[52]  Yuan-Cheng Lai,et al.  Identifying android malicious repackaged applications by thread-grained system call sequences , 2013, Comput. Secur..

[53]  Michael Backes,et al.  AppGuard - Fine-Grained Policy Enforcement for Untrusted Android Applications , 2013, DPM/SETOP.

[54]  Hao Chen,et al.  AnDarwin: Scalable Detection of Semantically Similar Android Applications , 2013, ESORICS.

[55]  Jules White,et al.  Applying machine learning classifiers to dynamic Android malware detection at scale , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[56]  Bing Mao,et al.  DroidAlarm: an all-sided static analysis tool for Android privilege-escalation malware , 2013, ASIA CCS '13.

[57]  Xuxian Jiang,et al.  DroidChameleon: evaluating Android anti-malware against transformation attacks , 2013, ASIA CCS '13.

[58]  Thomas Schreck,et al.  Mobile-sandbox: having a deeper look into android applications , 2013, SAC '13.

[59]  Yajin Zhou,et al.  Fast, scalable detection of "Piggybacked" mobile applications , 2013, CODASPY.

[60]  Igor Santos,et al.  Anomaly Detection Using String Analysis for Android Malware Detection , 2013, SOCO-CISIS-ICEUTE.

[61]  Hao Chen,et al.  Attack of the Clones: Detecting Cloned Applications on Android Markets , 2012, ESORICS.

[62]  Latifur Khan,et al.  A Machine Learning Approach to Android Malware Detection , 2012, 2012 European Intelligence and Security Informatics Conference.

[63]  Ross J. Anderson,et al.  Aurasium: Practical Policy Enforcement for Android Applications , 2012, USENIX Security Symposium.

[64]  Steve Hanna,et al.  Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications , 2012, DIMVA.

[65]  David A. Wagner,et al.  Android permissions: user attention, comprehension, and behavior , 2012, SOUPS.

[66]  Albert B. Jeng,et al.  Android Malware Detection via a Latent Network Behavior Analysis , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[67]  Yajin Zhou,et al.  RiskRanker: scalable and accurate zero-day android malware detection , 2012, MobiSys '12.

[68]  Ninghui Li,et al.  Android permissions: a perspective combining risks and benefits , 2012, SACMAT '12.

[69]  Jacques Klein,et al.  Dexpler: converting Android Dalvik bytecode to Jimple for static analysis with Soot , 2012, SOAP '12.

[70]  Yajin Zhou,et al.  Detecting repackaged smartphone applications in third-party android marketplaces , 2012, CODASPY '12.

[71]  Yajin Zhou,et al.  Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets , 2012, NDSS.

[72]  Barbara G. Ryder,et al.  User-Centric Dependence Analysis For Identifying Malicious Mobile Apps , 2012 .

[73]  Steve Hanna,et al.  Android permissions demystified , 2011, CCS '11.

[74]  Simin Nadjm-Tehrani,et al.  Crowdroid: behavior-based malware detection system for Android , 2011, SPSM '11.

[75]  Ondrej Lhoták,et al.  The Soot framework for Java program analysis: a retrospective , 2011 .

[76]  David A. Wagner,et al.  Analyzing inter-application communication in Android , 2011, MobiSys '11.

[77]  Yuval Elovici,et al.  “Andromaly”: a behavioral malware detection framework for android devices , 2012, Journal of Intelligent Information Systems.

[78]  Byung-Gon Chun,et al.  TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones , 2010, OSDI.

[79]  Paul C. van Oorschot,et al.  A methodology for empirical analysis of permission-based security models and its application to android , 2010, CCS '10.

[80]  Avik Chaudhuri,et al.  SCanDroid: Automated Security Certification of Android , 2009 .

[81]  Patrick D. McDaniel,et al.  On lightweight mobile phone application certification , 2009, CCS.