Hardening Quantum Machine Learning Against Adversaries
Authors: Nathan Wiebe, Ram Shankar Siva Kumar | Published: 2017-11-17
Enhanced Attacks on Defensively Distilled Deep Neural Networks
Authors: Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu | Published: 2017-11-16
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Authors: Yannic Kilcher, Thomas Hofmann | Published: 2017-11-15
CryptoDL: Deep Neural Networks over Encrypted Data
Authors: Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi | Published: 2017-11-14
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Authors: Jihun Hamm, Akshay Mehra | Published: 2017-11-12 | Updated: 2018-06-27
Dynamic Analysis of Executables to Detect and Characterize Malware
Authors: Michael R. Smith, Joe B. Ingram, Christopher C. Lamb, Timothy J. Draelos, Justin E. Doak, James B. Aimone, Conrad D. James | Published: 2017-11-10 | Updated: 2018-09-28
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning
Authors: Se Eun Oh, Saikrishna Sunkam, Nicholas Hopper | Published: 2017-11-10 | Updated: 2018-04-02
LatentPoison – Adversarial Attacks On The Latent Space
Authors: Antonia Creswell, Anil A. Bharath, Biswa Sengupta | Published: 2017-11-08
Intriguing Properties of Adversarial Examples
Authors: Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le | Published: 2017-11-08
Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques
Authors: Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova, Jill Slay | Published: 2017-11-08