GMM Based Semi-Supervised Learning for Channel-Based Authentication Scheme
GMM Based Semi-Supervised Learning for Channel-Based Authentication Scheme
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DOI：
10.1109/vtcfall.2013.6692216
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
Nikhil Gulati;R. Greenstadt;K. Dandekar;J. Walsh
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文献类型：
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作者：
Nikhil Gulati;R. Greenstadt;K. Dandekar;J. Walsh
近年来，基于无线物理层信道信息的认证方案得到了极大的关注。最近的研究表明，基于信道的认证既可以与现有的高层安全协议合作，也可以为传感器网络等没有中央权威的网络提供一定程度的安全性。我们提出了一种基于高斯混合模型的半监督学习技术，通过建立网络用户无线信道的概率模型来识别网络中的入侵者。我们表明，即使没有一个完整的先验知识的网络中的入侵者和用户的统计数据，我们的技术可以学习和更新的模型在一个在线的方式，同时保持高检测率。我们通过实验证明了我们提出的技术利用模式多样性，并显示使用测得的通道，误检率低至0.1%，误报率为0.3%，可以实现。
Authentication schemes based on wireless physical layer channel information have gained significant attention in recent years. It has been shown in recent studies, that the channel based authentication can either cooperate with existing higher layer security protocols or provide some degree of security to networks without central authority such as sensor networks. We propose a Gaussian Mixture Model based semi-supervised learning technique to identify intruders in the network by building a probabilistic model of the wireless channel of the network users. We show that even without having a complete apriori knowledge of the statistics of intruders and users in the network, our technique can learn and update the model in an online fashion while maintaining high detection rate. We experimentally demonstrate our proposed technique leveraging pattern diversity and show using measured channels that miss detection rates as low as 0.1% for false alarm rate of 0.3% can be achieved.