Explainability and Adversarial Robustness for RNNs

@article{Hartl2020ExplainabilityAA,
  title={Explainability and Adversarial Robustness for RNNs},
  author={Alexander Hartl and Maximilian Bachl and J. Fabini and T. Zseby},
  journal={2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)},
  year={2020},
  pages={148-156}
}
  • Alexander Hartl, Maximilian Bachl, +1 author T. Zseby
  • Published 2020
  • Computer Science, Mathematics
  • 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)
Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data. With the rise of ubiquitous Machine Learning (ML) systems, malicious actors have been catching up quickly to find new ways to exploit ML vulnerabilities for profit. Recently developed adversarial ML techniques focus on computer vision and their applicability to network traffic is not straightforward: Network packets expose fewer features than an image, are… Expand
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