Fetching the paper…
Reading the bibliography…
Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples.
A practical method for the direct analysis of transient stability
T Athay, R Podmore, and S Virmani · 1979
Earlier work this paper cites.
False data injection attacks against state estimation in electric power grids
Yao Liu, Peng Ning, and Michael K Reiter · 2009
Earlier work this paper cites.
Matpower: Steady-state operations, planning, and analysis tools for power systems research and education
Ray Daniel Zimmerman, Carlos Edmundo Murillo-Sánchez, and Robert John Thomas · 2010
Earlier work this paper cites.
Defending mechanisms against false-data injection attacks in the power system state estimation
Suzhi Bi and Ying Jun Zhang · 2011
Earlier work this paper cites.
Thermocast: A cyber-physical forecasting model for datacenters
Lei Li, Chieh-Jan Mike Liang, Jie Liu, Suman Nath, Andreas Terzis, and Christos Faloutsos · 2011
Earlier work this paper cites.
State estimation in electric power systems: a generalized approach
Alcir Monticelli · 2012
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Power generation, operation, and control
Allen J Wood, Bruce F Wollenberg, and Gerald B Sheblé · 2013
Earlier work this paper cites.
The loss surface of multilayer networks
Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, and Yann LeCun · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Practical evasion of a learning-based classifier: A case study
Nedim Rndic and Pavel Laskov · 2014
Earlier work this paper cites.
Droid-sec: deep learning in android malware detection
Zhenlong Yuan, Yongqiang Lu, Zhaoguo Wang, and Yibo Xue · 2014
Earlier work this paper cites.
Machine learning methods for attack detection in the smart grid
Mete Ozay, Inaki Esnaola, Fatos Tunay Yarman Vural, Sanjeev R Kulkarni, and H Vincent Poor · 2015
Earlier work this paper cites.
A dataset to support research in the design of secure water treatment systems
Jonathan Goh, Sridhar Adepu, Khurum Nazir Junejo, and Aditya Mathur · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
Masking transmission line outages via false data injection attacks
Xuan Liu, Zhiyi Li, Xingdong Liu, and Zuyi Li · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 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.
Adversarial diversity and hard positive generation
Andras Rozsa, Ethan M Rudd, and Terrance E Boult · 2016
Cited alongside, same era.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
Later among the works it cites.
Learning from mutants: Using code mutation to learn and monitor invariants of a cyber-physical system
Yuqi Chen, Christopher M Poskitt, and Jun Sun · 2018
Later among the works it cites.
State space models for forecasting water quality variables: an application in aquaculture prawn farming
Joel Janek Dabrowski, Ashfaqur Rahman, Andrew George, Stuart Arnold, and John McCulloch · 2018
Later among the works it cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Later among the works it cites.
Adversarial regression for detecting attacks in cyber-physical systems
Amin Ghafouri, Yevgeniy Vorobeychik, and Xenofon Koutsoukos · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
Cited alongside, same era.
Detection of false data attacks in smart grid with supervised learning
Jun Yan, Bo Tang, and Haibo He · 2016
Cited alongside, same era.
A deep learning-based framework for conducting stealthy attacks in industrial control systems
Cheng Feng, Tingting Li, Zhanxing Zhu, and Deeph Chana · 2017
Cited alongside, same era.
Adversarial examples for malware detection
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel · 2017
Cited alongside, same era.
Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism
Youbiao He, Gihan J Mendis, and Jin Wei · 2017
Cited alongside, same era.
Anomaly detection for a water treatment system using unsupervised machine learning
Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M Poskitt, and Jun Sun · 2017
Cited alongside, same era.
No need to worry about adversarial examples in object detection in autonomous vehicles
Jiajun Lu, Hussein Sibai, Evan Fabry, and David Forsyth · 2017
Cited alongside, same era.
Online false data injection attack detection with wavelet transform and deep neural networks
JQ James, Yunhe Hou, and Victor OK Li · 2018
Later among the works it cites.
Detecting cyber attacks in industrial control systems using convolutional neural networks
Moshe Kravchik and Asaf Shabtai · 2018
Later among the works it cites.
Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang · 2018
Later among the works it cites.
Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray · 2018
Later among the works it cites.
Alessandro Erba, Riccardo Taormina, Stefano Galelli, Marcello Pogliani, Michele Carminati, Stefano Zanero, and Nils Ole Tippenhauer · 2019
Later among the works it cites.
A systematic framework to generate invariants for anomaly detection in industrial control systems
Cheng Feng, Venkata Reddy Palleti, Aditya Mathur, and Deeph Chana · 2019
Later among the works it cites.
Secure Water Treatment (SWaT) Dataset
ITrust Labs · 2019
Later among the works it cites.
Dynamic detection of false data injection attack in smart grid using deep learning
Xiangyu Niu, Jiangnan Li, Jinyuan Sun, and Kevin Tomsovic · 2019
Later among the works it cites.
Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
Later among the works it cites.
IEEE 39-Bus System
Illinois Center for a Smarter Electric Grid · 2020
Closest in time.
Detection of false data injection attacks using the autoencoder approach
Chenguang Wang, Simon Tindemans, Kaikai Pan, and Peter Palensky · 2020
Closest in time.