  Machine learning algorithms are effective in several applications, but they
are not as much successful when applied to intrusion detection in cyber
security. Due to the high sensitivity to their training data, cyber detectors
based on machine learning are vulnerable to targeted adversarial attacks that
involve the perturbation of initial samples. Existing defenses assume
unrealistic scenarios; their results are underwhelming in non-adversarial
settings; or they can be applied only to machine learning algorithms that
perform poorly for cyber security. We present an original methodology for
countering adversarial perturbations targeting intrusion detection systems
based on random forests. As a practical application, we integrate the proposed
defense method in a cyber detector analyzing network traffic. The experimental
results on millions of labelled network flows show that the new detector has a
twofold value: it outperforms state-of-the-art detectors that are subject to
adversarial attacks; it exhibits robust results both in adversarial and
non-adversarial scenarios.
