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Found 5 papers, 3 papers with code
Realistic Website Fingerprinting By Augmenting Network Trace
Our extensive open-world and close-world experiments demonstrate that under practical evaluation settings, our WF attacks provide superior performances compared to the state-of-the-art; this is due to their use of augmented network traces for training, which allows them to learn the features of target traffic in unobserved settings.
Truveta Mapper: A Zero-shot Ontology Alignment Framework
In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task.
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
We show that in the presence of defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems.
Adversarial Attack
Cryptography and Security
Blind Adversarial Network Perturbations
Deep Neural Networks (DNNs) are commonly used for various traffic analysis problems, such as website fingerprinting and flow correlation, as they outperform traditional (e. g., statistical) techniques by large margins.
DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning
Flow correlation is the core technique used in a multitude of deanonymization attacks on Tor.
