Effectiveness of Opcode ngrams for Detection of Multi Family Android Malware
暂无分享,去创建一个
Eric Medvet | Gerardo Canfora | Corrado Aaron Visaggio | Andrea De Lorenzo | Francesco Mercaldo | G. Canfora | C. A. Visaggio | Eric Medvet | A. D. Lorenzo | F. Mercaldo
[1] Dan Arp,et al. Drebin : � Efficient and Explainable Detection of Android Malware in Your Pocket , 2014 .
[2] Isil Dillig,et al. Apposcopy: semantics-based detection of Android malware through static analysis , 2014, SIGSOFT FSE.
[3] Babak Bashari Rad,et al. Metamorphic Virus Variants Classification Using Opcode Frequency Histogram , 2011, ArXiv.
[4] Thomas Schreck,et al. Mobile-sandbox: having a deeper look into android applications , 2013, SAC '13.
[5] Jiqiang Liu,et al. A Two-Layered Permission-Based Android Malware Detection Scheme , 2014, 2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering.
[6] Ninghui Li,et al. Android permissions: a perspective combining risks and benefits , 2012, SACMAT '12.
[7] Ninghui Li,et al. Using probabilistic generative models for ranking risks of Android apps , 2012, CCS.
[8] Yajin Zhou,et al. Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.
[9] R. Ramachandran,et al. Android Anti-Virus Analysis , .
[10] Arun Lakhotia,et al. DroidLegacy: Automated Familial Classification of Android Malware , 2014, PPREW'14.
[11] Xiaojiang Du,et al. Permission-combination-based scheme for Android mobile malware detection , 2014, 2014 IEEE International Conference on Communications (ICC).
[12] Yoseba K. Penya,et al. N-grams-based File Signatures for Malware Detection , 2009, ICEIS.
[13] Zhong Chen,et al. AutoCog: Measuring the Description-to-permission Fidelity in Android Applications , 2014, CCS.
[14] Sakir Sezer,et al. A New Android Malware Detection Approach Using Bayesian Classification , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).
[15] Tao Xie,et al. WHYPER: Towards Automating Risk Assessment of Mobile Applications , 2013, USENIX Security Symposium.
[16] Yajin Zhou,et al. Android Malware , 2013, SpringerBriefs in Computer Science.
[17] Shih-Hao Hung,et al. DroidDolphin: a dynamic Android malware detection framework using big data and machine learning , 2014, RACS '14.
[18] Xuxian Jiang,et al. DroidChameleon: evaluating Android anti-malware against transformation attacks , 2013, ASIA CCS '13.
[19] Radu State,et al. Using opcode-sequences to detect malicious Android applications , 2014, 2014 IEEE International Conference on Communications (ICC).
[20] U. Bayer,et al. TTAnalyze: A Tool for Analyzing Malware , 2006 .
[21] Eric Medvet,et al. Compressing Regular Expression Sets for Deep Packet Inspection , 2014, PPSN.
[22] M. Masrom,et al. Opcodes histogram for classifying metamorphic portable executables malware , 2012, 2012 International Conference on E-Learning and E-Technologies in Education (ICEEE).
[23] Konrad Rieck,et al. DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.
[24] Ram Dantu,et al. Another free app: Does it have the right intentions? , 2014, 2014 Twelfth Annual International Conference on Privacy, Security and Trust.
[25] Eul Gyu Im,et al. Malware classification using instruction frequencies , 2011, RACS.
[26] Vijay Laxmi,et al. AndroSimilar: robust statistical feature signature for Android malware detection , 2013, SIN.
[27] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[28] Ohm Sornil,et al. Classification of malware families based on N-grams sequential pattern features , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).
[29] Gerardo Canfora,et al. A Classifier of Malicious Android Applications , 2013, 2013 International Conference on Availability, Reliability and Security.
[30] Daniel Bilar,et al. Opcodes as predictor for malware , 2007, Int. J. Electron. Secur. Digit. Forensics.