Explaining Black-box Android Malware Detection
暂无分享,去创建一个
Fabio Roli | Marco Melis | Giorgio Giacinto | Battista Biggio | Davide Maiorca | F. Roli | B. Biggio | Davide Maiorca | G. Giacinto | Marco Melis
[1] L. Breiman. SOME INFINITY THEORY FOR PREDICTOR ENSEMBLES , 2000 .
[2] Motoaki Kawanabe,et al. How to Explain Individual Classification Decisions , 2009, J. Mach. Learn. Res..
[3] Vern Paxson,et al. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection , 2010, 2010 IEEE Symposium on Security and Privacy.
[4] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[5] Konrad Rieck,et al. DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket , 2014, NDSS.
[6] Dan Arp,et al. Drebin : � Efficient and Explainable Detection of Android Malware in Your Pocket , 2014 .
[7] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[8] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[9] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[10] Minhui Xue,et al. StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware , 2016, AsiaCCS.
[11] Fabio Roli,et al. Secure Kernel Machines against Evasion Attacks , 2016, AISec@CCS.
[12] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[13] Michael Backes,et al. LUNA: Quantifying and Leveraging Uncertainty in Android Malware Analysis through Bayesian Machine Learning , 2017, 2017 IEEE European Symposium on Security and Privacy (EuroS&P).
[14] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[15] Seth Flaxman,et al. European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" , 2016, AI Mag..
[16] Fabio Roli,et al. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2018, CCS.
[17] Juan E. Tapiador,et al. Picking on the family: Disrupting android malware triage by forcing misclassification , 2018, Expert Syst. Appl..
[18] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[19] Fabio Roli,et al. Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection , 2017, IEEE Transactions on Dependable and Secure Computing.