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A framework for empirical evaluation of malware detection resilience against behavior obfuscation.

, , , , , и . MALWARE, стр. 40-47. IEEE Computer Society, (2015)

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Malware detection with quantitative data flow graphs., , и . AsiaCCS, стр. 271-282. ACM, (2014)Q-Floid: Android Malware detection with Quantitative Data Flow Graphs., , , и . SG-CRC, том 14 из Cryptology and Information Security Series, стр. 13-25. IOS Press, (2016)Leveraging Compression-Based Graph Mining for Behavior-Based Malware Detection., , , и . IEEE Trans. Dependable Secur. Comput., 16 (1): 99-112 (2019)FEEBO: An Empirical Evaluation Framework for Malware Behavior Obfuscation., , , , и . CoRR, (2015)DAVAST: data-centric system level activity visualization., , и . VizSEC, стр. 25-32. ACM, (2014)Behavior-based Malware Detection with Quantitative Data Flow Analysis.. Technical University Munich, Germany, (2016)A framework for empirical evaluation of malware detection resilience against behavior obfuscation., , , , , и . MALWARE, стр. 40-47. IEEE Computer Society, (2015)Robust and Effective Malware Detection Through Quantitative Data Flow Graph Metrics., , и . DIMVA, том 9148 из Lecture Notes in Computer Science, стр. 98-118. Springer, (2015)MalFlow: identification of C&C servers through host-based data flow profiling., , , , , и . SAC, стр. 2087-2094. ACM, (2016)Data Loss Prevention Based on Data-Driven Usage Control., и . ISSRE, стр. 151-160. IEEE Computer Society, (2012)