Fetching the paper…
Reading the bibliography…
Recent results suggest that attacks against supervised machine learning systems are quite effective, while defenses are easily bypassed by new attacks.
Systematic poisoning attacks on and defenses for machine learning in healthcare
1905
Earlier work this paper cites.
The mnist database of handwritten digits
1998
Earlier work this paper cites.
Building natural language generation systems
2000
Earlier work this paper cites.
Casting out demons: Sanitizing training data for anomaly sensors
2008
Earlier work this paper cites.
Exploiting machine learning to subvert your spam filter
2008
Earlier work this paper cites.
Machine learning in the presence of an adversary: Attacking and defending the spambayes spam filter
2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
2009
Earlier work this paper cites.
A game theoretical model for adversarial learning
2009
Earlier work this paper cites.
The security of machine learning
2010
Earlier work this paper cites.
Stranger danger - introducing smartscreen application reputation
2010
Earlier work this paper cites.
@ spam: the underground on 140 characters or less
2010
Earlier work this paper cites.
Stackelberg games for adversarial prediction problems
2011
Earlier work this paper cites.
Polonium : Tera-scale graph mining for malware detection
2011
Earlier work this paper cites.
Adversarial machine learning
2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
2012
Earlier work this paper cites.
Abuse at scale
2012
Earlier work this paper cites.
Adapting social spam infrastructure for political censorship
2012
Earlier work this paper cites.
Data breach investigations reports (dbir), February 2012
2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
2013
Earlier work this paper cites.
CAMP: Content-agnostic malware protection
2013
Cited alongside, same era.
Intriguing properties of neural networks
2013
Cited alongside, same era.
Drebin: Effective and explainable detection of android malware in your pocket
2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
2014
Cited alongside, same era.
Practical evasion of a learning-based classifier: A case study
2014
Cited alongside, same era.
Guilt by association: large scale malware detection by mining file-relation graphs
2014
Cited alongside, same era.
Automatically evading classifiers
2016
Later among the works it cites.
Understanding deep learning requires rethinking generalization
2016
Later among the works it cites.
2016
Later among the works it cites.
The multilayered security model in kaspersky lab products, Mar 2017
2017
Later among the works it cites.
Explaining how a deep neural network trained with end-to-end learning steers a car
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
How transferable are features in deep neural networks?
2014
Cited alongside, same era.
The future of underwriting
2015
Cited alongside, same era.
Cloudy with a chance of breach: Forecasting cyber security incidents
2015
Cited alongside, same era.
Vulnerability disclosure in the age of social media: exploiting twitter for predicting real-world exploits
2015
Cited alongside, same era.
FICO enterprise security score gives long-term view of cyber risk exposure, November 2016
2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
2016
Cited alongside, same era.
2017
Later among the works it cites.
Towards evaluating the robustness of neural networks
2017
Later among the works it cites.
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
2017
Later among the works it cites.
Assisting pathologists in detecting cancer with deep learning
2017
Later among the works it cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
2017
Later among the works it cites.
Understanding black-box predictions via influence functions
2017
Later among the works it cites.
Trojaning attack on neural networks
2017
Later among the works it cites.
How to upgrade judges with machine learning
2017
Later among the works it cites.
Towards poisoning of deep learning algorithms with back-gradient optimization
2017
Later among the works it cites.
Practical black-box attacks against deep learning systems using adversarial examples
2017
Later among the works it cites.
Certified defenses for data poisoning attacks
2017
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
2017
Later among the works it cites.
Generative poisoning attack method against neural networks
2017
Later among the works it cites.