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Machine Learning based Decision Stratigies for Physical Layer Authentication in Wireless Systems

机译：基于机器学习的无线系统物理层认证的决策策略

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摘要

In this paper, machine learning (ML) based decision strategies for physical layer authentication are presented. The intelligent authenticators learn the channel features and then classify the received message based on the channel attributes into two categories, legitimate or illegitimate. The training set construction using different features of the estimated channel fading coefficients explored. In addition, ML based physical layer authentication is compared with the statistical discriminative function formulated in binary hypothesis test with a pre-defined threshold. Simulation results demonstrated that the performance of intelligent authenticators outperform the statistical decision scheme as significant improvement can be achieved in the detection rate with minimum false alarm rate. The overall authentication accuracy measured in terms of the area under the receiver operating characteristic curve (AVC) confirmed the superior performance of the the support vector machine (SVM) based physical layer authentication compared with other ML approaches. In addition, it is concluded that using two distinct features improves the authentication performance compared with feature space constructed only from test statistic metrics.
机译：本文介绍了基于机器学习（ML）物理层认证的决策策略。智能身份验证器学习通道功能，然后将接收的消息基于频道属性分为两类，合法或非法。使用估计频道衰落系数的不同特征探索的训练集结构。另外，将基于ML的物理层认证与具有预定阈值预定阈值的二元假设试验中配制的统计辨别功能进行比较。仿真结果表明，智能验证器的性能优于统计决策方案，因为可以以最小的误报率在检测速率下实现显着改进。根据接收器操作特性曲线（AVC）下的区域测量的整体认证精度证实了与其他ML方法相比的基于支持向量机（SVM）的物理层认证的优异性能。此外，结论是，使用两个不同的特征可提高身份验证性能与仅从测试统计指标构造的特征空间相比。

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