Robust representation for domain adaptation in network security

Abstract

The goal of domain adaptation is to solve the problem of different joint distribution of observation and labels in the training and testing data sets. This problem happens in many practical situations such as when a malware detector is trained from labeled datasets at certain time point but later evolves to evade detection. We solve the problem by introducing a new representation which ensures that a conditional distribution of the observation given labels is the same. The representation is computed for bags of samples (network traffic logs) and is designed to be invariant under shifting and scaling of the feature values extracted from the logs and under permutation and size changes of the bags. The invariance of the representation is achieved by relying on a self-similarity matrix computed for each bag. In our experiments, we will show that the representation is effective for training detector of malicious traffic in large corporate networks. Compared to the case without domain adaptation, the recall of the detector improves from 0.81 to 0.88 and precision from 0.998 to 0.999.