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Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations
IEEE Transactions on Cognitive Communications and Networking ( IF 8.6 ) Pub Date : 2020-01-01 , DOI: 10.1109/tccn.2020.3043332 Samer Hanna , Samurdhi Karunaratne , Danijela Cabric
IEEE Transactions on Cognitive Communications and Networking ( IF 8.6 ) Pub Date : 2020-01-01 , DOI: 10.1109/tccn.2020.3043332 Samer Hanna , Samurdhi Karunaratne , Danijela Cabric
Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, the majority of the work has focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this paper, we consider the problem of recognizing authorized transmitters and rejecting new transmitters. To address this problem, we adapt the most prominent approaches from the open set recognition and anomaly detection literature to the problem. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The robustness of the RF authorization with respect to temporal changes in fingerprints is also considered in the evaluation. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.
中文翻译:
开放式无线发射器授权:深度学习方法和数据集考虑
由于发射器硬件的缺陷,无线信号可用于在授权系统中验证其身份。虽然提出了用于识别发射机的深度学习,但大部分工作都集中在一组封闭的发射机中进行分类。此封闭集之外的恶意发送者将被错误分类,从而危及授权系统。在本文中,我们考虑识别授权发射器和拒绝新发射器的问题。为了解决这个问题,我们将开放集识别和异常检测文献中最突出的方法应用于该问题。我们研究了这些方法如何根据所需的授权发射机数量进行扩展。我们建议使用一组已知的未经授权的发射器来协助训练和研究其影响。评估程序考虑到一些发射机可能比其他发射机更相似,并细微差别这些影响。评估中还考虑了 RF 授权相对于指纹时间变化的稳健性。当使用来自可公开访问的测试平台的 10 个授权和 50 个已知的未授权 WiFi 发射器时,我们能够在同一天测试集上实现 98% 的异常检测准确度,在不同天测试集上实现 80%。
更新日期:2020-01-01
中文翻译:
开放式无线发射器授权:深度学习方法和数据集考虑
由于发射器硬件的缺陷,无线信号可用于在授权系统中验证其身份。虽然提出了用于识别发射机的深度学习,但大部分工作都集中在一组封闭的发射机中进行分类。此封闭集之外的恶意发送者将被错误分类,从而危及授权系统。在本文中,我们考虑识别授权发射器和拒绝新发射器的问题。为了解决这个问题,我们将开放集识别和异常检测文献中最突出的方法应用于该问题。我们研究了这些方法如何根据所需的授权发射机数量进行扩展。我们建议使用一组已知的未经授权的发射器来协助训练和研究其影响。评估程序考虑到一些发射机可能比其他发射机更相似,并细微差别这些影响。评估中还考虑了 RF 授权相对于指纹时间变化的稳健性。当使用来自可公开访问的测试平台的 10 个授权和 50 个已知的未授权 WiFi 发射器时,我们能够在同一天测试集上实现 98% 的异常检测准确度,在不同天测试集上实现 80%。