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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., , , , , and . MALWARE, page 40-47. IEEE Computer Society, (2015)FEEBO: An Empirical Evaluation Framework for Malware Behavior Obfuscation., , , , and . CoRR, (2015)DAVAST: data-centric system level activity visualization., , and . VizSEC, page 25-32. ACM, (2014)Leveraging Compression-Based Graph Mining for Behavior-Based Malware Detection., , , and . IEEE Trans. Dependable Sec. Comput., 16 (1): 99-112 (2019)Malware detection with quantitative data flow graphs., , and . AsiaCCS, page 271-282. ACM, (2014)Q-Floid: Android Malware detection with Quantitative Data Flow Graphs., , , and . SG-CRC, volume 14 of Cryptology and Information Security Series, page 13-25. IOS Press, (2016)Generating behavior-based malware detection models with genetic programming., , , and . PST, page 506-511. IEEE, (2016)The Disability-Simulator: Simulating the Influences of Disabilities on the Usability of Graphical User Interfaces., , and . HCI (19), volume 6779 of Lecture Notes in Computer Science, page 109-118. Springer, (2011)MalFlow: identification of C&C servers through host-based data flow profiling., , , , , and . SAC, page 2087-2094. ACM, (2016)