Robust adversarial learning and invariant measures

@article{Neville2015RobustAL,
  title={Robust adversarial learning and invariant measures},
  author={Stephen W. Neville and Mohamed Elgamal and Zahra Nikdel},
  journal={2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)},
  year={2015},
  pages={529-535}
}
A number of open cyber-security challenges are arising due to the rapidly evolving scale, complexity, and heterogeneity of modern IT systems and networks. The ease with which copious volumes of operational data can be collected from such systems has produced a strong interest in the use of machine learning (ML) for cyber-security, provided that ML can… CONTINUE READING