7 citations
3 papers
cs.LG2019
Deep Detector Health Management under Adversarial Campaigns
Javier Echauz, Keith Kenemer, Sarfaraz Hussein +4
Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control system…
cs.CR2018
Detecting DGA domains with recurrent neural networks and side information
Ryan R. Curtin, Andrew B. Gardner, Slawomir Grzonkowski +2
Modern malware typically makes use of a domain generation algorithm (DGA) to avoid command and control domains or IPs being seized or sinkholed. This means that an infected system…
cs.CV2018★ 7 cited
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning
Jasjeet Dhaliwal, Saurabh Shintre
Deep neural networks are susceptible to small-but-specific adversarial perturbations capable of deceiving the network. This vulnerability can lead to potentially harmful consequenc…