ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Authors: Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu | Published: 2019-05-28
Controlling Neural Level Sets
Authors: Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman | Published: 2019-05-28 | Updated: 2019-10-27
Snooping Attacks on Deep Reinforcement Learning
Authors: Matthew Inkawhich, Yiran Chen, Hai Li | Published: 2019-05-28 | Updated: 2020-01-15
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics
Authors: Yi Xiang Marcus Tan, Alfonso Iacovazzi, Ivan Homoliak, Yuval Elovici, Alexander Binder | Published: 2019-05-28 | Updated: 2019-11-27
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models
Authors: Soham Deshmukh, Rahul Rade, Faruk Kazi | Published: 2019-05-28 | Updated: 2021-06-06
Expected Tight Bounds for Robust Training
Authors: Salman Alsubaihi, Adel Bibi, Modar Alfadly, Abdullah Hamdi, Bernard Ghanem | Published: 2019-05-28 | Updated: 2021-06-12
Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set
Authors: Simon Duque Anton, Suneetha Kanoor, Daniel Fraunholz, Hans Dieter Schotten | Published: 2019-05-28
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Authors: Pengcheng Li, Jinfeng Yi, Bowen Zhou, Lijun Zhang | Published: 2019-05-28
Implementing SCADA Scenarios and Introducing Attacks to Obtain Training Data for Intrusion Detection Methods
Authors: Simon Duque Antón, Michael Gundall, Daniel Fraunholz, Hans Dieter Schotten | Published: 2019-05-28
Adversarially Robust Learning Could Leverage Computational Hardness
Authors: Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody | Published: 2019-05-28 | Updated: 2019-12-19