AndroDialysis: Analysis of Android Intent Effectiveness in Malware Detection
暂无分享,去创建一个
Ali Feizollah | Nor Badrul Anuar | Rosli Salleh | Steven Furnell | Guillermo Suarez-Tangil | N. B. Anuar | S. Furnell | Guillermo Suarez-Tangil | Ali Feizollah | R. Salleh
[1] Niu Yan,et al. A3: Automatic Analysis of Android Malware , 2013, CloudCom 2013.
[2] Concha Bielza,et al. Discrete Bayesian Network Classifiers , 2014, ACM Comput. Surv..
[3] Hojung Cha,et al. DevScope: a nonintrusive and online power analysis tool for smartphone hardware components , 2012, CODES+ISSS.
[4] Sakir Sezer,et al. Analysis of Bayesian classification-based approaches for Android malware detection , 2016, IET Inf. Secur..
[5] Kun Chang Lee,et al. Exploring the Optimal Path to Online Game Loyalty: Bayesian Networks versus Theory-Based Approaches , 2011, UCMA.
[6] Heng Yin,et al. DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android , 2013, SecureComm.
[7] Silva Filho,et al. Static analysis of implicit control flow: resolving Java reflection and Android intents , 2016 .
[8] Hojung Cha,et al. AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring , 2012, USENIX Annual Technical Conference.
[9] Ali Feizollah,et al. Evaluation of machine learning classifiers for mobile malware detection , 2014, Soft Computing.
[10] Aiman Abu Samra,et al. Analysis of Clustering Technique in Android Malware Detection , 2013, 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.
[11] Ali Feizollah,et al. A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection , 2013 .
[12] Paul C. van Oorschot,et al. A methodology for empirical analysis of permission-based security models and its application to android , 2010, CCS '10.
[13] Veelasha Moonsamy,et al. Mining permission patterns for contrasting clean and malicious android applications , 2014, Future Gener. Comput. Syst..
[14] Nicu Sebe,et al. Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[15] Matthew L. Dering,et al. Composite Constant Propagation: Application to Android Inter-Component Communication Analysis , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[16] Zhen Huang,et al. PScout: analyzing the Android permission specification , 2012, CCS.
[17] Anthony Desnos,et al. Android: Static Analysis Using Similarity Distance , 2012, 2012 45th Hawaii International Conference on System Sciences.
[18] Chun-Ying Huang,et al. Performance Evaluation on Permission-Based Detection for Android Malware , 2013 .
[19] Chao Yang,et al. DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications , 2014, ESORICS.
[20] Win Zaw,et al. Permission-Based Android Malware Detection , 2013 .
[21] Michael Backes,et al. AppGuard - Enforcing User Requirements on Android Apps , 2013, TACAS.
[22] Maria Papadaki,et al. Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers , 2012, Secur. Commun. Networks.
[23] Jacques Klein,et al. IccTA: Detecting Inter-Component Privacy Leaks in Android Apps , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[24] Yuan Zhang,et al. Vetting undesirable behaviors in android apps with permission use analysis , 2013, CCS.
[25] Tao Xie,et al. WHYPER: Towards Automating Risk Assessment of Mobile Applications , 2013, USENIX Security Symposium.
[26] Lior Rokach,et al. Mobile malware detection through analysis of deviations in application network behavior , 2014, Comput. Secur..
[27] Christopher Krügel,et al. TriggerScope: Towards Detecting Logic Bombs in Android Applications , 2016, 2016 IEEE Symposium on Security and Privacy (SP).
[28] Patrick Traynor,et al. MAST: triage for market-scale mobile malware analysis , 2013, WiSec '13.
[29] Yajin Zhou,et al. Android Malware , 2013, SpringerBriefs in Computer Science.
[30] Hahn-Ming Lee,et al. DroidMat: Android Malware Detection through Manifest and API Calls Tracing , 2012, 2012 Seventh Asia Joint Conference on Information Security.
[31] Konrad Rieck,et al. DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.
[32] Ninghui Li,et al. Using probabilistic generative models for ranking risks of Android apps , 2012, CCS.
[33] Marcelo d'Amorim,et al. Static Analysis of Implicit Control Flow: Resolving Java Reflection and Android Intents (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[34] R. F. Brown,et al. PERFORMANCE EVALUATION , 2019, ISO 22301:2019 and business continuity management – Understand how to plan, implement and enhance a business continuity management system (BCMS).
[35] Michalis Faloutsos,et al. ProfileDroid: multi-layer profiling of android applications , 2012, Mobicom '12.
[36] David A. Wagner,et al. Analyzing inter-application communication in Android , 2011, MobiSys '11.
[37] Aristide Fattori,et al. CopperDroid: Automatic Reconstruction of Android Malware Behaviors , 2015, NDSS.
[38] Juan E. Tapiador,et al. Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families , 2014, Expert Syst. Appl..
[39] Yajin Zhou,et al. Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets , 2012, NDSS.
[40] David Heckerman,et al. Learning Bayesian Networks: Search Methods and Experimental Results , 1995 .
[41] Isil Dillig,et al. Apposcopy: semantics-based detection of Android malware through static analysis , 2014, SIGSOFT FSE.
[42] Ainuddin Wahid Abdul Wahab,et al. A review on feature selection in mobile malware detection , 2015, Digit. Investig..
[43] Pedro Larrañaga,et al. Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[44] Gonzalo Álvarez,et al. PUMA: Permission Usage to Detect Malware in Android , 2012, CISIS/ICEUTE/SOCO Special Sessions.
[45] Nick Cercone,et al. Bayesian network modeling for evolutionary genetic structures , 2010, Comput. Math. Appl..
[46] Juan E. Tapiador,et al. Power-aware anomaly detection in smartphones: An analysis of on-platform versus externalized operation , 2015, Pervasive Mob. Comput..
[47] Nir Friedman,et al. Bayesian Network Classifiers , 1997, Machine Learning.
[48] Yajin Zhou,et al. RiskRanker: scalable and accurate zero-day android malware detection , 2012, MobiSys '12.
[49] Mansour Ahmadi,et al. Clustering android malware families by http traffic , 2015, 2015 10th International Conference on Malicious and Unwanted Software (MALWARE).