基于隐马尔可夫模型的传感器认证机器学习方法

2019 Eighth International Conference on Emerging Security Technologies (EST) Pub Date : 2019-07-01 DOI:10.1109/EST.2019.8806200

J. Murphy, G. Howells, K. Mcdonald-Maier

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引用次数: 1

摘要

提出了一种基于传感器认证的机器学习方法。它利用隐马尔可夫模型从可变传感器数据生成稳定的综合概率密度函数。详细介绍了新方法的原理和新颖性，并进行了统计评价。结果表明，使用隐马尔可夫模型可以显著提高系统的稳定性。

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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A Machine Learning Method For Sensor Authentication Using Hidden Markov Models

A machine learning method for sensor based authentication is presented. It exploits hidden markov models to generate stable and synthetic probability density functions from variant sensor data. The principle, and novelty, of the new method are presented in detail together with a statistical evaluation. The results show a marked improvement in stability through the use of hidden markov models.

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来源期刊

2019 Eighth International Conference on Emerging Security Technologies (EST)

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