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Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly.
Rearchitecting Classification Frameworks For Increased Robustness, 2019
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Learning in the presence of malicious errors
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Optimal layered learning: A PAC approach to incremental sampling
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Scaling up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid
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Learning in the presence of concept drift and hidden contexts
Gerhard Widmer and Miroslav Kubat · 1996
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Wide-area Internet traffic patterns and characteristics
Kevin Thompson, Gregory J Miller, and Rick Wilder · 1997
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Adversarial reinforcement learning
William Uther and Manuela Veloso · 1997
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OPTICS: Ordering Points to Identify the Clustering Structure
Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander · 1999
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Cost-based modeling for fraud and intrusion detection: results from the JAM project
S.J. Stolfo, Wei Fan, Wenke Lee, A. Prodromidis, and P.K. Chan · 2000
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Internet traffic measurement
Carey Williamson · 2001
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Learning with Drift Detection
João Gama, Pedro Medas, Gladys Castillo, and Pedro Rodrigues · 2004
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A detailed analysis of the KDD CUP 99 data set
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani · 2009
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Adversarial Examples in Constrained Domains
Ryan Sheatsley, Nicolas Papernot, Michael Weisman, Gunjan Verma, and Patrick McDaniel · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection
O.Y. Al-Jarrah, A. Siddiqui, M. Elsalamouny, P.D. Yoo, S. Muhaidat, and K. Kim · 2014
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Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
Daniel Arp, Michael Spreitzenbarth, Malte Hübner, Hugo Gascon, and Konrad Rieck · 2014
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ICE: A Robust Framework for Learning Invariants
Pranav Garg, Christof Löding, P. Madhusudan, and Daniel Neider · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Data Poisoning Attacks against Autoregressive Models
Scott Alfeld, Xiaojin Zhu, and Paul Barford · 2016
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A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
A. L. Buczak and E. Guven · 2016
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JB Heaton, Nicholas G Polson, and Jan Hendrik Witte · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
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cleverhans v1.0.0: an adversarial machine learning library
Nicolas Papernot, Ian Goodfellow, Ryan Sheatsley, Reuben Feinman, and Patrick McDaniel · 2016
Cited alongside, same era.
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
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Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
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Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Cited alongside, same era.
Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang · 2016
Cited alongside, same era.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
Cited alongside, same era.
Characterizing concept drift
Geoffrey I Webb, Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean · 2016
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
Cited alongside, same era.
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
To cripple AI, hackers are turning data against itself
Nicole Kobie · 2018
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Adversarial malware binaries: Evading deep learning for malware detection in executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli · 2018
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IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection
Zilong Lin, Yong Shi, and Zhi Xue · 2018
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Google Develops ’Adversarial Example’ Images that Fool Both Humans and Computers
Kimberley Mok · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems
K. Yang, J. Liu, C. Zhang, and Y. Fang · 2018
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A Data-Driven CHC Solver
He Zhu, Stephen Magill, and Suresh Jagannathan · 2018
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A new hybrid ensemble feature selection framework for machine learning-based phishing detection system
Kang Leng Chiew, Choon Lin Tan, KokSheik Wong, Kelvin S.C. Yong, and Wei King Tiong · 2019
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Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean · 2019
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Fooling the machine
Dave Gershgorn · 2019
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Science Museum curator picks five designs for a driverless future
Natashah Hitti · 2019
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What Happens When Television News Gets The Deep Fake Treatment?
Kalev Leetaru · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani · 2019
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A general framework for adversarial examples with objectives
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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