An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications

@article{Beaver2013AnEO,
  title={An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications},
  author={Justin M. Beaver and Raymond C. Borges-Hink and Mark A. Buckner},
  journal={2013 12th International Conference on Machine Learning and Applications},
  year={2013},
  volume={2},
  pages={54-59}
}
Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems have been designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning… CONTINUE READING

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