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Found 4 papers, 1 papers with code
STAN: Synthetic Network Traffic Generation with Generative Neural Models
We evaluate the performance of STAN in terms of the quality of data generated, by training it on both a simulated dataset and a real network traffic data set.
Attention-Based Self-Supervised Feature Learning for Security Data
While applications of machine learning in cyber-security have grown rapidly, most models use manually constructed features.
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors.
Modeling Scalability of Distributed Machine Learning
It is implemented in a distributed fashion in order to address these scalability issues.
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