Fetching the paper…
Reading the bibliography…
Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as image recognition.
Probabilistic Counting Algorithms for Data Base Applications
Philippe Flajolet and G. Nigel Martin. 1985 · 1985
Earlier work this paper cites.
Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau. 1991 · 1991
Earlier work this paper cites.
Regression Shrinkage and Selection via the Lasso
Robert Tibshirani. 1996 · 1996
Earlier work this paper cites.
Neural network applications in business: A review and analysis of the literature (1988–1995)
Bo K Wong, Thomas A Bodnovich, and Yakup Selvi. 1997 · 1997
Earlier work this paper cites.
Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions
Guang-Bin Huang and Haroon A Babri. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998 · 1998
Earlier work this paper cites.
An application of machine learning to network intrusion detection. In Computer Security Applications Conference, 1999.(ACSAC’99) Proceedings. 15th Annual . IEEE, 371–377
Chris Sinclair, Lyn Pierce, and Sara Matzner. 1999 · 1999
Earlier work this paper cites.
Business data mining—a machine learning perspective
Indranil Bose and Radha K Mahapatra. 2001 · 2001
Earlier work this paper cites.
HotFlip: White-Box Adversarial Examples for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, 31–36
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2006
Earlier work this paper cites.
Machine learning approach to authorship attribution of literary texts
Urszula Stańczyk and Krzysztof A Cyran. 2007 · 2007
Earlier work this paper cites.
Authorship attribution
Patrick Juola et al · 2008
Earlier work this paper cites.
The WEKA data mining software: an update
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009 · 2009
Earlier work this paper cites.
A detailed analysis of the KDD CUP 99 data set. In Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on . IEEE, 1–6
Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A Ghorbani. 2009 · 2009
Earlier work this paper cites.
Intrusion detection by machine learning: A review
Chih-Fong Tsai, Yu-Feng Hsu, Chia-Ying Lin, and Wei-Yang Lin. 2009 · 2009
Earlier work this paper cites.
Outside the closed world: On using machine learning for network intrusion detection. In Security and Privacy (SP), 2010 IEEE Symposium on . IEEE, 305–316
Robin Sommer and Vern Paxson. 2010 · 2010
Earlier work this paper cites.
Sketch techniques for approximate query processing. In Synposes for Approximate Query Processing: Samples, Histograms, Wavelets and Sketches, Foundations and Trends in Databases. NOW publishers
Graham Cormode. 2011 · 2011
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. 2012 · 2012
Cited alongside, same era.
Evasion attacks against machine learning at test time. In Joint European conference on machine learning and knowledge discovery in databases . Springer, 387–402
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 2013 · 2013
Cited alongside, same era.
A comparison study for intrusion database (Kdd99, Nsl-Kdd) based on self organization map (SOM) artificial neural network
Laheeb M Ibrahim, Dujan T Basheer, and Mahmod S Mahmod. 2013 · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
Cited alongside, same era.
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017 · 2017
Later among the works it cites.
Towards evaluating the robustness of neural networks. In Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 39–57
Nicholas Carlini and David Wagner. 2017 · 2017
Later among the works it cites.
Attacking Machine Learning with Adversarial Examples
Ian Goodfellow, Nicolas Papernot, Sandy Huang, Peter Duan, Pieter Abbeel, and Jack Clark. 2017 · 2017
Later among the works it cites.
Adversarial examples for malware detection. In European Symposium on Research in Computer Security . Springer, 62–79
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel. 2017 · 2017
Later among the works it cites.
Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples
Jamie Hayes and George Danezis. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Machine learning applications in cancer prognosis and prediction
Konstantina Kourou, Themis P. Exarchos, Konstantinos P. Exarchos, Michalis V. Karamouzis, and Dimitrios I. Fotiadis. 2015 · 2014
Cited alongside, same era.
A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms
L. Dhanabal and Dr. S. P. Shantharajah. 2015 · 2015
Cited alongside, same era.
Explaining and Harnessing Adversarial Examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Performance analysis of NSL-KDD dataset using ANN. In Signal Processing And Communication Engineering Systems (SPACES), 2015 International Conference on . IEEE, 92–96
Bhupendra Ingre and Anamika Yadav. 2015 · 2015
Cited alongside, same era.
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In Military Communications and Information Systems Conference (MilCIS), 2015 . IEEE, 1–6
Nour Moustafa and Jill Slay. 2015 · 2015
Cited alongside, same era.
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2574–2582
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard. 2016 · 2016
Cited alongside, same era.
The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set
Nour Moustafa and Jill Slay. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Adversarial examples in the physical world. In International Conference on Learning Representations Workshop
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2017 · 2017
Later among the works it cites.
Universal Adversarial Perturbations. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 86–94
S. M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard. 2017 · 2017
Later among the works it cites.
A hybrid feature selection for network intrusion detection systems: Central points
Nour Moustafa and Jill Slay. 2017 · 2017
Later among the works it cites.
The Space of Transferable Adversarial Examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2017 · 2017
Later among the works it cites.
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Nicholas Carlini and David A. Wagner. 2018 · 2018
Later among the works it cites.
Learning constraints from examples
Luc De Raedt, Andrea Passerini, and Stefano Teso. 2018 · 2018
Later among the works it cites.
Black-box Adversarial Attacks with Limited Queries and Information. In Proceedings of the 35th International Conference on Machine Learning, { \{ ICML } \} 2018
Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin, Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. 2018 · 2018
Later among the works it cites.
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables. In 2018 26th European Signal Processing Conference (EUSIPCO) . 533–537
B. Kolosnjaji, A. Demontis, B. Biggio, D. Maiorca, G. Giacinto, C. Eckert, and F. Roli. 2018 · 2018
Later among the works it cites.
Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Later among the works it cites.
One Pixel Attack for Fooling Deep Neural Networks
J. Su, D. V. Vargas, and K. Sakurai. 2019 · 2019
Later among the works it cites.
Adversarial Examples for Evaluating Reading Comprehension Systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2021–2031
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.