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Fighting Cybercrime by Introducing Trustworthiness and Interpretability in Deep Learning Malware Detection.

, , and . ERCIM News, 2022 (129): 0 (2022)

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Visualizing the outcome of dynamic analysis of Android malware with VizMal., , , , and . J. Inf. Secur. Appl., (2020)Android Collusion: Detecting Malicious Applications Inter-Communication through SharedPreferences., , , and . Inf., 11 (6): 304 (2020)Machine Learning for Driver Detection through CAN bus., , and . VTC Spring, page 1-5. IEEE, (2020)Experimenting and assessing machine learning tools for detecting and analyzing malicious behaviors in complex environments., , , and . J. Reliab. Intell. Environ., 4 (4): 225-245 (2018)Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection., , , and . IWSPA@CODASPY, page 50-57. ACM, (2016)Identifying Mobile Repackaged Applications through Formal Methods., , , , and . ICISSP, page 673-682. SciTePress, (2017)Malware and Formal Methods: Rigorous Approaches for detecting Malicious Behaviour., , , and . ARES, page 76:1-76:6. ACM, (2017)Evaluating Deep Learning Classification Reliability in Android Malware Family Detection., , , and . ISSRE Workshops, page 255-260. IEEE, (2020)Computational cost of CT Radiomics workflow: a case study on COVID-19., , , , and . COMPSAC, page 1539-1544. IEEE, (2023)Assessing Deep Learning Predictions in Image-Based Malware Detection with Activation Maps., , , and . STM, volume 13867 of Lecture Notes in Computer Science, page 104-114. Springer, (2022)