  Machine learning models have been widely used in security applications such
as intrusion detection, spam filtering, and virus or malware detection.
However, it is well-known that adversaries are always trying to adapt their
attacks to evade detection. For example, an email spammer may guess what
features spam detection models use and modify or remove those features to avoid
detection. There has been some work on making machine learning models more
robust to such attacks. However, one simple but promising approach called {\em
randomization} is underexplored. This paper proposes a novel
randomization-based approach to improve robustness of machine learning models
against evasion attacks. The proposed approach incorporates randomization into
both model training time and model application time (meaning when the model is
used to detect attacks). We also apply this approach to random forest, an
existing ML method which already has some degree of randomness. Experiments on
intrusion detection and spam filtering data show that our approach further
improves robustness of random-forest method. We also discuss how this approach
can be applied to other ML models.
