  Purveyors of malicious network attacks continue to increase the complexity
and the sophistication of their techniques, and their ability to evade
detection continues to improve as well. Hence, intrusion detection systems must
also evolve to meet these increasingly challenging threats. Machine learning is
often used to support this needed improvement. However, training a good
prediction model can require a large set of labelled training data. Such
datasets are difficult to obtain because privacy concerns prevent the majority
of intrusion detection agencies from sharing their sensitive data. In this
paper, we propose the use of mimic learning to enable the transfer of intrusion
detection knowledge through a teacher model trained on private data to a
student model. This student model provides a mean of publicly sharing knowledge
extracted from private data without sharing the data itself. Our results
confirm that the proposed scheme can produce a student intrusion detection
model that mimics the teacher model without requiring access to the original
dataset.
