航空电子网络异常检测的潜在狄利克雷分配(LDA)方法

2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC) Pub Date : 2020-10-11 DOI:10.1109/DASC50938.2020.9256582

Adam Thornton, Brandon Meiners, Donald Poole

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引用次数: 3

摘要

针对地面车辆网络，采用潜狄利克雷分配(Latent Dirichlet Allocation, LDA)和变分推理(Variational Inference)近实时检测地面车辆网络流量异常。描述了利用自然语言处理(NLP)技术检测潜在恶意攻击和网络配置问题的技术方法，并提供了概念实现证明的结果。提供了在飞机和航空电子领域应用该技术的潜在用例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Latent Dirichlet Allocation (LDA) for Anomaly Detection in Avionics Networks

Latent Dirichlet Allocation (LDA) and Variational Inference are applied in near real-time to detect anomalies in ground vehicle network traffic for a ground vehicle network. 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. Potential use cases for applying the technique in the aircraft and avionics domain are provided.

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来源期刊

2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC)

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