基于多算法特征级融合方案虹膜验证的物联网安全

Ramadan Gad, A. A. Abd El-Latif, S. Elseuofi, H. M. Ibrahim, M. Elmezain, Wael Said
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引用次数: 17

摘要

物联网(IoT)在商业和生活中有非常大的机会,特别是网上银行、医疗保健和交通运输。但开发人员在安全方面面临挑战,特别是在敏感数据安全使用物联网与互联网银行方面。本文将提出一种基于虹膜识别系统(认证服务器)的客户端与代理服务器之间的安全物联网连接。提出的虹膜识别系统将与MQTT代理服务器通信,以增加身份验证,而不是使用普通的文本方法(用户名/密码)。本文提出的认证服务器是利用虹膜作为单模态生物特征,通过应用多模态以外的多种生物特征的不同场景,开发和实现基于虹膜的识别系统。在特征提取阶段,以并行方式进行Delta-Mean (DM)和Multi-Algorithm-Mean (MAM)向量的融合。然后,采用本文提出的约简方法,减小了特征向量的大小,保持了较高的性能。最后,采用欧式距离(ED)分类器进行分类。它适用于安全MQTT连接消息对银行转账操作等敏感应用程序中的正确客户端进行身份验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IoT Security Based on Iris Verification Using Multi-Algorithm Feature Level Fusion Scheme
Internet of Things (IoT) have very big opportunities in businesses and life, especially Internet Banking, Healthcare and Transportation. But developers face challenges in security especially for sensitive data to use IoT safely with Internet Banking. In this paper, a secure IoT connection between clients and broker server based on iris recognition system (Authentication server) will be proposed. The proposed iris recognition system will communicate with MQTT broker server to increase authentication instead of using normal text method (username/password). The proposed authentication server in this paper is to use iris as uni-modal biometric trait, by applying different scenarios of multi-biometrics other than the multimodal, to develop and implement iris-based recognition system. In feature extraction phase, the fusion between vectors of Delta-Mean (DM) and Multi-Algorithm-Mean (MAM) executed in a parallel mode. Then, followed by the proposed reduction method, reduced the feature vector size keeping higher performance. Finally, classification is adopted by using Euclidian Distance (ED) classifier. It is suitable for secure MQTT connection message to authenticate right client in sensitive application like bank transfer operations.
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