Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
暂无分享,去创建一个
Liang Tong | Yevgeniy Vorobeychik | Bo Li | Chaowei Xiao | Ning Zhang | Chen Hajaj | Bo Li | Chaowei Xiao | Liang Tong | Chen Hajaj | Yevgeniy Vorobeychik | Ning Zhang
[1] Fabio Roli,et al. Security Evaluation of Pattern Classifiers under Attack , 2014, ArXiv.
[2] Pavel Laskov,et al. Detection of Malicious PDF Files Based on Hierarchical Document Structure , 2013, NDSS.
[3] Atul Prakash,et al. Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Wenke Lee,et al. Polymorphic Blending Attacks , 2006, USENIX Security Symposium.
[5] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[6] Pedro M. Domingos,et al. Adversarial classification , 2004, KDD.
[7] J. Doug Tygar,et al. Evasion and Hardening of Tree Ensemble Classifiers , 2015, ICML.
[8] Patrick P. K. Chan,et al. Adversarial Feature Selection Against Evasion Attacks , 2016, IEEE Transactions on Cybernetics.
[9] Dale Schuurmans,et al. Learning with a Strong Adversary , 2015, ArXiv.
[10] Pavel Laskov,et al. Hidost: a static machine-learning-based detector of malicious files , 2016, EURASIP J. Inf. Secur..
[11] Aditi Raghunathan,et al. Certified Defenses against Adversarial Examples , 2018, ICLR.
[12] Tobias Scheffer,et al. Static prediction games for adversarial learning problems , 2012, J. Mach. Learn. Res..
[13] J. Zico Kolter,et al. Provable defenses against adversarial examples via the convex outer adversarial polytope , 2017, ICML.
[14] Giorgio Giacinto,et al. Looking at the bag is not enough to find the bomb: an evasion of structural methods for malicious PDF files detection , 2013, ASIA CCS '13.
[15] Bhavani M. Thuraisingham,et al. Adversarial support vector machine learning , 2012, KDD.
[16] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[17] Blaine Nelson,et al. Can machine learning be secure? , 2006, ASIACCS '06.
[18] Pavel Laskov,et al. Practical Evasion of a Learning-Based Classifier: A Case Study , 2014, 2014 IEEE Symposium on Security and Privacy.
[19] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[20] Tobias Scheffer,et al. Stackelberg games for adversarial prediction problems , 2011, KDD.
[21] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[22] Bo Li,et al. Evasion-Robust Classification on Binary Domains , 2018, ACM Trans. Knowl. Discov. Data.
[23] Patrick D. McDaniel,et al. Adversarial Perturbations Against Deep Neural Networks for Malware Classification , 2016, ArXiv.
[24] Yevgeniy Vorobeychik,et al. Optimal randomized classification in adversarial settings , 2014, AAMAS.
[25] Murat Kantarcioglu,et al. Adversarial Machine Learning , 2018, Adversarial Machine Learning.
[26] Ling Huang,et al. Query Strategies for Evading Convex-Inducing Classifiers , 2010, J. Mach. Learn. Res..
[27] Christopher Meek,et al. Adversarial learning , 2005, KDD '05.
[28] Wenke Lee,et al. Evading network anomaly detection systems: formal reasoning and practical techniques , 2006, CCS '06.
[29] Patrick D. McDaniel,et al. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.
[30] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[31] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[32] Shie Mannor,et al. Robustness and Regularization of Support Vector Machines , 2008, J. Mach. Learn. Res..
[33] Alexander J. Smola,et al. Convex Learning with Invariances , 2007, NIPS.
[34] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[35] Thomas Stützle,et al. Stochastic Local Search: Foundations & Applications , 2004 .
[36] Christopher Krügel,et al. Detection and analysis of drive-by-download attacks and malicious JavaScript code , 2010, WWW '10.
[37] Yanjun Qi,et al. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers , 2016, NDSS.
[38] Angelos Stavrou,et al. Malicious PDF detection using metadata and structural features , 2012, ACSAC '12.
[39] Michael P. Wellman,et al. Towards the Science of Security and Privacy in Machine Learning , 2016, ArXiv.
[40] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).