Latent Dirichlet Allocation (LDA) for Anomaly Detection in Ground Vehicle Network Traffic
Adam Thornton, Brandon Mieners, Donald R. Poole, Mark Russell · SAE technical papers on CD-ROM/SAE technical paper series · 2020
ABSTRACT Latent Dirichlet Allocation (LDA) and Variational Inference are applied in near real-time to detect anomalies in ground vehicle network traffic for VICTORY enabled networks. The technical approach, that utilizes the Natural Language Processing (NLP) technique to detect potential malicious attacks and network configuration issues, is described and the results of a proof of concept implementation are provided. Citation: A. Thornton, B. Meiners, D. Poole, M. Russell, “Latent Dirichlet Allocation (LDA) for Anomaly Detection in Ground Vehicle Network Traffic”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2019.