Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection

Abstract:Malicious anomalous activity detection is a fundamental challenge for cyber security systems. Both tensor decomposition under statistical framework with CANDECOMP-PARAFAC alternating Poisson regression (CP-APR) and normalizing flows have proven to be powerful unsupervised machine learning methods that model multi-dimensional data and capture complex and multi-faceted details of behavior profiles in cyber security applications. In this study, we propose Hybrid Latent-Structural Fusion (HLSF), a weighted anomaly fusion framework integrating CP-APR structural anomaly scores with latent-space density scores derived from normalizing flows. In our experiments, we show that the HLSF framework improves anomaly detection performance on a dataset of real-world compromised user credentials collected from the large enterprise network of Los Alamos National Laboratory (LANL) during a red-teaming exercise, compared with using CP-APR or normalizing flows alone.

Machine Learning (cs.LG)

arXiv:2607.18479 [cs.LG]

(or arXiv:2607.18479v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.18479 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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