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A Cross-Layer Biometric Recognition System for Mobile IoT Devices

机译:移动物联网设备的跨层生物识别系统

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A biometric recognition system is one of the leading candidates for the current and the next generation of smart visual systems. The visual system is the engine of the surveillance cameras that have great importance for intelligence and security purposes. These surveillance devices can be a target of adversaries for accomplishing various malicious scenarios such as disabling the camera in critical times or the lack of recognition of a criminal. In this work, we propose a cross-layer biometric recognition system that has small computational complexity and is suitable for mobile Internet of Things (IoT) devices. Furthermore, due to the involvement of both hardware and software in realizing this system in a decussate and chaining structure, it is easier to locate and provide alternative paths for the system flow in the case of an attack. For security analysis of this system, one of the elements of this system named the advanced encryption standard (AES) is infected by four different Hardware Trojansthat target different parts of this module. The purpose of these Trojans is to sabotage the biometric data that are under process by the biometric recognition system. All of the software and the hardware modules of this system are implemented using MATLAB and Verilog HDL, respectively. According to the performance evaluation results, the system shows an acceptable performance in recognizing healthy biometric data. It is able to detect the infected data, as well. With respect to its hardware results, the system may not contribute significantly to the hardware design parameters of a surveillance camera considering all the hardware elements within the device.
机译:生物识别系统是当前和下一代智能视觉系统的主要候选者之一。视觉系统是监控摄像头的引擎,对于智能和安全目的而言非常重要。这些监视设备可能成为攻击者的目标,以实现各种恶意情况,例如在关键时刻禁用摄像机或对犯罪分子的识别不足。在这项工作中,我们提出了一种跨层生物识别系统,该系统具有较低的计算复杂度,并且适用于移动物联网(IoT)设备。此外,由于在讨论和链接结构中实现该系统都涉及硬件和软件,因此在遭受攻击的情况下,更容易找到系统流并为其提供替代路径。为了对该系统进行安全性分析,该系统的一个名为高级加密标准(AES)的元素被针对该模块不同部分的四个不同的硬件木马感染。这些木马的目的是破坏生物识别系统正在处理的生物数据。该系统的所有软件和硬件模块分别使用MATLAB和Verilog HDL实现。根据性能评估结果,该系统在识别健康的生物特征数据方面显示出可接受的性能。它还能够检测受感染的数据。就其硬件结果而言,考虑到设备内的所有硬件元素,系统可能不会对监控摄像机的硬件设计参数做出重大贡献。

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