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Classification of ransomware families with machine learning based on N-gram of opcodes., , , , , and . Future Gener. Comput. Syst., (2019)Distributed Data Anonymization., and . DASC/PiCom/DataCom/CyberSciTech, page 580-586. IEEE, (2019)Introducing Usage Control in MQTT., , , , and . CyberICPS/SECPRE@ESORICS, volume 10683 of Lecture Notes in Computer Science, page 35-43. Springer, (2017)Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques., , , and . IoTBDS, page 499-506. SCITEPRESS, (2020)Dependability in dynamic, evolving and heterogeneous systems: the connect approach., , , , and . SERENE, page 83-88. ACM, (2010)On absence of diffusion for low energy for a random Schrödinger operator on L2(R), and . Physica A: Statistical and Theoretical Physics, 124 (1-3): 413--417 (March 1984)Mobile Silent and Continuous Authentication using Apps Sequence., , , , , , and . SECRYPT, page 79-91. SciTePress, (2017)Machine Learning Meets iOS Malware: Identifying Malicious Applications on Apple Environment., , and . ICISSP, page 487-492. SciTePress, (2017)I find your behavior disturbing: Static and dynamic app behavioral analysis for detection of Android malware., , , and . PST, page 129-136. IEEE, (2016)Quantitative access control with partially-observable Markov decision processes., and . CODASPY, page 169-180. ACM, (2012)