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Found 5 papers, 1 papers with code
Deep Detector Health Management under Adversarial Campaigns
Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors.
Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection
Our approach is to build a neural network model utilizing Adversarial Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who leverages off-the-shelf tools and system applications.
Detecting DGA domains with recurrent neural networks and side information
Our experiments show the model is capable of effectively identifying domains generated by difficult DGA families.
Enabling Trust in Deep Learning Models: A Digital Forensics Case Study
Today, the volume of evidence collected per case is growing exponentially, to address this problem forensics investigators are looking for investigation process with tools built on new technologies like big data, cloud services, and Deep Learning (DL) techniques.
Cryptography and Security
On Detecting Messaging Abuse in Short Text Messages using Linguistic and Behavioral patterns
The use of short text messages in social media and instant messaging has become a popular communication channel during the last years.
