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Zero Day Threats (ZDT) are novel methods used by malicious actors to attackand exploit information technology (IT) networks or infrastructure. In the pastfew years, the number of these threats has been increasing at an alarming rateand have been costing organizations millions of dollars to remediate. Theincreasing expansion of network attack surfaces and the exponentially growingnumber of assets on these networks necessitate the need for a robust AI-basedZero Day Threat detection model that can quickly analyze petabyte-scale datafor potentially malicious and novel activity. In this paper, the authorsintroduce a deep learning based approach to Zero Day Threat detection that cangeneralize, scale, and effectively identify threats in near real-time. Themethodology utilizes network flow telemetry augmented with asset-level graphfeatures, which are passed through a dual-autoencoder structure for anomaly andnovelty detection respectively. The models have been trained and tested on fourlarge scale datasets that are representative of real-world organizationalnetworks and they produce strong results with high precision and recall values.The models provide a novel methodology to detect complex threats with lowfalse-positive rates that allow security operators to avoid alert fatigue whiledrastically reducing their mean time to response with near-real-time detection.Furthermore, the authors also provide a novel, labelled, cyber attack datasetgenerated from adversarial activity that can be used for validation or trainingof other models. With this paper, the authors' overarching goal is to provide anovel architecture and training methodology for cyber anomaly detectors thatcan generalize to multiple IT networks with minimal to no retraining whilestill maintaining strong performance.

Zero Day Threat Detection Using Graph and Flow Based Security Telemetry