Search Results for author:
Found 5 papers, 1 papers with code
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods.
Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders
We present a deep learning based approach to containerized application runtime stability analysis, and an intelligent publishing algorithm that can dynamically adjust the depth of process-level forensics published to a backend incident analysis repository.
Data Masking with Privacy Guarantees
In particular, we propose a method of masking the private data with privacy guarantee while ensuring that a classifier trained on the masked data is similar to the classifier trained on the original data, to maintain usability.
Time Series Deinterleaving of DNS Traffic
Stream deinterleaving is an important problem with various applications in the cybersecurity domain.
Trusted Neural Networks for Safety-Constrained Autonomous Control
Experiments using our first approach of a multi-headed TNN model, on a dataset generated by a customized version of TORCS, show that (1) adding safety constraints to a neural network model results in increased performance and safety, and (2) the improvement increases with increasing importance of the safety constraints.
