Adversarial Machine Learning
暂无分享,去创建一个
[1] Fabio Roli,et al. Secure Kernel Machines against Evasion Attacks , 2016, AISec@CCS.
[2] Yanjun Qi,et al. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers , 2016, NDSS.
[3] Angelos Stavrou,et al. Malicious PDF detection using metadata and structural features , 2012, ACSAC '12.
[4] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..
[5] Blaine Nelson,et al. Can machine learning be secure? , 2006, ASIACCS '06.
[6] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[7] C. Eckart,et al. The approximation of one matrix by another of lower rank , 1936 .
[8] Steve Hanna,et al. Juxtapp: A Scalable System for Detecting Code Reuse among Android Applications , 2012, DIMVA.
[9] Marius Kloft,et al. Security analysis of online centroid anomaly detection , 2010, J. Mach. Learn. Res..
[10] Constantine Caramanis,et al. Robust PCA via Outlier Pursuit , 2010, IEEE Transactions on Information Theory.
[11] I. Jolliffe. Principal Component Analysis , 2005 .
[12] Bo Li,et al. Evasion-Robust Classification on Binary Domains , 2018, ACM Trans. Knowl. Discov. Data.
[13] Shie Mannor,et al. Robust Regression and Lasso , 2008, IEEE Transactions on Information Theory.
[14] Xinming Huang,et al. End-to-end learning for lane keeping of self-driving cars , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).
[15] J. D. Arias-Londoño,et al. Fraud detection in big data using supervised and semi-supervised learning techniques , 2017, 2017 IEEE Colombian Conference on Communications and Computing (COLCOM).
[16] Kaizhu Huang,et al. A Unified Gradient Regularization Family for Adversarial Examples , 2015, 2015 IEEE International Conference on Data Mining.
[17] Shyamanta M. Hazarika,et al. E-Mail Spam Filtering: A Review of Techniques and Trends , 2018 .
[18] Shobha Venkataraman,et al. Efficient Solution Algorithms for Factored MDPs , 2003, J. Artif. Intell. Res..
[19] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[20] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Wenke Lee,et al. Evading network anomaly detection systems: formal reasoning and practical techniques , 2006, CCS '06.
[22] Rocco A. Servedio,et al. Learning Halfspaces with Malicious Noise , 2009, ICALP.
[23] Chris Clifton,et al. Classifier evaluation and attribute selection against active adversaries , 2010, Data Mining and Knowledge Discovery.
[24] Seyed-Mohsen Moosavi-Dezfooli,et al. Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[26] Marco Saerens,et al. A graph-based, semi-supervised, credit card fraud detection system , 2016, COMPLEX NETWORKS.
[27] Pavel Laskov,et al. Hidost: a static machine-learning-based detector of malicious files , 2016, EURASIP J. Inf. Secur..
[28] Salvatore J. Stolfo,et al. Anagram: A Content Anomaly Detector Resistant to Mimicry Attack , 2006, RAID.
[29] Emmanuel J. Candès,et al. A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..
[30] Duen Horng Chau,et al. Guilt by association: large scale malware detection by mining file-relation graphs , 2014, KDD.
[31] Chang Liu,et al. Robust Linear Regression Against Training Data Poisoning , 2017, AISec@CCS.
[32] Patrick P. K. Chan,et al. Adversarial Feature Selection Against Evasion Attacks , 2016, IEEE Transactions on Cybernetics.
[33] Garth P. McCormick,et al. Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems , 1976, Math. Program..
[34] Ryan O'Donnell,et al. Some topics in analysis of boolean functions , 2008, STOC.
[35] S. Sitharama Iyengar,et al. A Survey on Malware Detection Using Data Mining Techniques , 2017, ACM Comput. Surv..
[36] Nathan Linial,et al. The influence of variables on Boolean functions , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.
[37] Pedro M. Domingos,et al. Adversarial classification , 2004, KDD.
[38] Fabio Roli,et al. Security Evaluation of Pattern Classifiers under Attack , 2014, IEEE Transactions on Knowledge and Data Engineering.
[39] Murat Kantarcioglu,et al. Modeling Adversarial Learning as Nested Stackelberg Games , 2016, PAKDD.
[40] John D. Montgomery. Spoofing, Market Manipulation, and the Limit-Order Book , 2016 .
[41] Ronald de Wolf,et al. A Brief Introduction to Fourier Analysis on the Boolean Cube , 2008, Theory Comput..
[42] Eyal Kushilevitz,et al. PAC learning with nasty noise , 1999, Theor. Comput. Sci..
[43] Craig Boutilier,et al. Stochastic dynamic programming with factored representations , 2000, Artif. Intell..
[44] Rocco A. Servedio,et al. Agnostically learning halfspaces , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).
[45] 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.
[46] Blaine Nelson,et al. The security of machine learning , 2010, Machine Learning.
[47] Yevgeniy Vorobeychik,et al. Multidefender Security Games , 2015, IEEE Intelligent Systems.
[48] Hans Ulrich Simon,et al. Robust Trainability of Single Neurons , 1995, J. Comput. Syst. Sci..
[49] Prateek Jain,et al. Low-rank matrix completion using alternating minimization , 2012, STOC '13.
[50] I. Jolliffe. A Note on the Use of Principal Components in Regression , 1982 .
[51] Rocco A. Servedio,et al. Smooth Boosting and Learning with Malicious Noise , 2001, J. Mach. Learn. Res..
[52] Claudia Eckert,et al. Adversarial Label Flips Attack on Support Vector Machines , 2012, ECAI.
[53] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[54] Tobias Scheffer,et al. Stackelberg games for adversarial prediction problems , 2011, KDD.
[55] Ming Li,et al. Learning in the Presence of Malicious Errors , 1993, SIAM J. Comput..
[56] Shie Mannor,et al. Robustness and Regularization of Support Vector Machines , 2008, J. Mach. Learn. Res..
[57] Shie Mannor,et al. Outlier-Robust PCA: The High-Dimensional Case , 2013, IEEE Transactions on Information Theory.
[58] Ling Huang,et al. Classifier Evasion: Models and Open Problems , 2010, PSDML.
[59] Christopher Meek,et al. Adversarial learning , 2005, KDD '05.
[60] Christophe Diot,et al. Diagnosing network-wide traffic anomalies , 2004, SIGCOMM.
[61] Claudia Eckert,et al. Support vector machines under adversarial label contamination , 2015, Neurocomputing.
[62] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[63] Peter J. Haas,et al. Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.
[64] Fabio Roli,et al. Poisoning Complete-Linkage Hierarchical Clustering , 2014, S+SSPR.
[65] Roberto Perdisci,et al. Scalable fine-grained behavioral clustering of HTTP-based malware , 2013, Comput. Networks.
[66] Salvatore J. Stolfo,et al. Casting out Demons: Sanitizing Training Data for Anomaly Sensors , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).
[67] Bhavani M. Thuraisingham,et al. Adversarial support vector machine learning , 2012, KDD.
[68] David Stevens,et al. On the hardness of evading combinations of linear classifiers , 2013, AISec.
[69] Ling Huang,et al. ANTIDOTE: understanding and defending against poisoning of anomaly detectors , 2009, IMC '09.
[70] Pavel Laskov,et al. Practical Evasion of a Learning-Based Classifier: A Case Study , 2014, 2014 IEEE Symposium on Security and Privacy.
[71] Christos H. Papadimitriou,et al. Strategic Classification , 2015, ITCS.