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Found 8 papers, 5 papers with code
Machine Learning Model Attribution Challenge
We present the findings of the Machine Learning Model Attribution Challenge.
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Recent years have seen a proliferation of research on adversarial machine learning.
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths.
Automatic Yara Rule Generation Using Biclustering
Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts.
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files.
Cryptography and Security
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning
We show in experiments that our method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset.
Cryptography and Security
Predicting Domain Generation Algorithms with Long Short-Term Memory Networks
Another technique to stop malware from using DGAs is to intercept DNS queries on a network and predict whether domains are DGA generated.
DeepDGA: Adversarially-Tuned Domain Generation and Detection
We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs.
