Abstract
On the path to establishing a global cybersecurity framework where each enterprise shares information about malicious behavior, an important question arises. How can a machine learning representation characterizing a cyber attack on one network be used to detect similar attacks on other enterprise networks if each networks has wildly different distributions of benign and malicious traffic? We address this issue by comparing the results of naively transferring a model across network domains and using CORrelation ALignment, to our novel adversarial Siamese neural network. Our proposed model learns attack representations that are more invariant to each network's particularities via an adversarial approach. It uses a simple ranking loss that prioritizes the labeling of the most egregious malicious events correctly over average accuracy. This is appropriate for driving an alert triage workflow wherein an analyst only has time to inspect the top few events ranked highest by the model. In ter