Cybersecurity threat detection based on a UEBA framework using Deep Autoencoders

TL;DR

This paper presents an explainable UEBA framework using Deep Autoencoders and Doc2Vec for anomaly detection, demonstrating effective detection of real and synthetic security threats with interpretability.

Contribution

It introduces the first explainable UEBA-based anomaly detection framework combining Deep Autoencoders with Doc2Vec and provides a novel proof of neural network equivalence.

Findings

Effective detection of real and synthetic anomalies

Models provide explainable results and origin reconstruction

Framework can be integrated into enterprise security systems

Abstract

User and Entity Behaviour Analytics (UEBA) is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep Autoencoders constitute one of the most promising deep learning models on UEBA tasks, allowing explainable detection of security incidents that could lead to the leak of personal data, hijacking of systems, or access to sensitive business information. In this study, we introduce the first implementation of an explainable UEBA-based anomaly detection framework that leverages Deep Autoencoders in combination with Doc2Vec to process both numerical and textual features. Additionally, based on the theoretical foundations of neural networks, we offer a novel proof demonstrating the equivalence of two widely used definitions for fully-connected neural networks. The experimental results demonstrate the proposed framework capability to detect real and synthetic anomalies effectively generated from real attack data, showing that the models provide not only correct identification of anomalies but also explainable results that enable the reconstruction of the possible origin of the anomaly. Our findings suggest that the proposed UEBA framework can be seamlessly integrated into enterprise environments, complementing existing security systems for explainable threat detection.