Reading List (new papers will be added...)
- "Keyboard emanations revisited,"
L. Zhuang, F. Zhou, J. D. Tygar.,
CCS 2005,
PDF
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"Mining Anomalies Using Traffic Feature Distributions,"
A. Lakhina, M. Crovella, and C. Diot,
SIGCOMM 2005,
PDF
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"Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning",
Barzan Mozafari, Purna Sarkar, Michael Franklin, Michael Jordan, Samuel Madden,
PVLDB 2014,
PDF
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"Adversarial Machine Learning",
Ling Huang, Anthony Joseph, Blaine Nelson, B. Rubinstein, J. D. Tygar,
AISec 2011,
PDF
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"Poisoning Attacks against Support Vector Machines,"
Battista Biggio, Blaine Nelson, Pavel Laskov,
ICML 2012,
PDF
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"Bayesian Watermark Attacks," Ivo Shterev, David Dunson,
ICML 2012,
PDF
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"Learning to Identify Regular Expressions that Describe Email Campaigns,"
Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer,
ICML 2012,
PDF
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"Finding Botnets Using Minimal Graph Clusterings,"
Peter Haider, Tobias Scheffer,
ICML 2012,
PDF
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"Towards evaluating the robustness of neural networks,"
Carlini, N., And Wagner, D.,
IEEE SP 2017,
PDF
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"Practical black-box attacks against machine learning,"
Papernot, N., Mcdaniel, P. D., Goodfellow, I. J., Jha, S., Celik, Z. B., And Swami, A.,
ASIA CCS 2017,
PDF
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"Approaches to adversarial drift,"
Kantchelian, A., Afroz, S., Huang, L., Islam, A. C., Miller, B., Tschantz, M. C., Green- Stadt, R., Joseph, A. D., And Tygar, J. D.,
AISec 2013,
PDF
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"Automatically evading classifiers,"
W Xu, Y Qi, D Evans,
NDSS 2016,
PDF
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"Adversarial classification,"
Dalvi, N., Domingos, P., Mausam, Sanghai, S., And Verma, D.,
KDD 2004,
PDF
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"Antidote: understanding and defending against poisoning of anomaly detectors,"
Rubinstein, B. I., Nelson, B., Huang, L., Joseph, A. D., Lau, S.-H., Rao, S., Taft, N., And Tygar, J. D.,
IMC 2009,
PDF
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"Transcend: Detecting Concept Drift in Malware Classification Models",
Roberto Jordaney, Royal Holloway, Kumar Sharad, Santanu K. Dash, Zhi Wang, Davide Papini, Ilia Nouretdinov, Lorenzo Cavallaro, Royal Holloway,
USENIX Security 2017,
PDF
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"Why should i trust you?: Explaining the predictions of any classifier,"
Ribeiro, M. T., Singh, S., And Guestrin, C.,
KDD 2016,
PDF
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"MagNet: a Two-Pronged Defense against Adversarial Examples,"
Dongyu Meng, Hao Chen,
CCS 2017,
PDF
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"Evading Classifiers by Morphing in the Dark,"
Hung Dang, Yue Huang, Ee-Chien Chang,
CCS 2017,
PDF
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"Hidden Voice Commands,"
Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David Wagner, Wenchao Zhou,
USENIX Security 2016,
PDF
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"Accessorize to a Crime: Real and Stealthy Attacks on
State-of-the-Art Face Recognition,"
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. Reiter,
CCS 2016,
PDF
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"Detecting Credential Spearphishing in Enterprise Settings,"
Grant Ho, Aashish Sharma, Mobin Javed, Vern Paxson, David Wagner,
USENIX Security 2017,
PDF
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"SAMPLES: Self Adaptive Mining of Persistent Lexical Snippets for Classifying Mobile Application Traffic,"
Hongyi Yao, Gyan Ranjan, Alok Tongaonkar, Yong Liao, Z. Morley Mao,
Mobicom 2015,
PDF
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"Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,"
A. Nguyen, J. Yosinski, J. Clune,
CVPR 2015
PDF
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"Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks,"
N. Papernot, P. McDaniel, X. Wu, S. Jha and A. Swami,
IEEE SP 2016,
PDF
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"Behavioral clustering of HTTP-based malware and signature generation using malicious network traces,"
Roberto Perdisci, Wenke Lee, and Nick Feamster,
NSDI 2010,
PDF
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"Doppelgänger Finder: Taking Stylometry to the Underground,"
Sadia Afroz, Aylin Caliskan Islam, Ariel Stolerman, Rachel Greenstadt, and Damon McCoy,
IEEE SP 2014,
PDF
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"Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks,"
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami.,
IEEE SP 2016,
PDF
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"Vulnerability disclosure in the age of social media: Exploiting Twitter for predicting real-world exploits,"
C. Sabottke, O. Suciu, and T. Dumitras,
USENIX Security 2015,
PDF