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Statistical learning theory relies on an assumption that the joint distributions of observations and labels are the same in training and testing data. However, this assumption is violated in many real world problems, such as training a detector of malicious network traffic that can change over time as a result of attacker’s detection evasion efforts. We propose to address this problem by creating an optimized representation, which significantly increases the robustness of detectors or classifiers trained under this distributional shift. The representation is created from bags of samples (e.g. 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 is achieved by combining feature histograms with feature self-similarity matrices computed for each bag and significantly reduces the difference between the training and testing data. The parameters of the representation, such as histogram bin boundaries, are learned jointly with the classifier. We show that the representation is effective for training a detector of malicious traffic, achieving 90% precision and 67% recall on samples of previously unseen malware variants.

Learning Invariant Representation for Malicious Network Traffic Detection