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Toward Secure Microfluidic Fully Programmable Valve Array Biochips

机译:迈向安全的微流控完全可编程阀阵列生物芯片

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The fully programmable valve array (FPVA) is a general-purpose programmable flow-based microfluidic platform, akin to the VLSI field-programmable gate array (FPGA). FPVAs are dynamically reconfigurable and, hence, are suitable in a broad spectrum of applications involving immunoassays and cell analysis. Since these applications are safety critical, addressing security concerns is vital for the success and adoption of FPVAs. This study evaluates the security of FPVA biochips. We show that FPVAs are vulnerable to malicious operations similar to digital and flow-based microfluidic biochips. FPVAs are further prone to new classes of attacks-tunneling and deliberate aging. This study establishes security metrics and describes possible attacks on real-life bioassays. Furthermore, we study the use of machine learning (ML) techniques to detect and classify attacks based on the golden and real-time biochip state. In order to boost the classifier's performance, we propose a smart checkpointing mechanism. Experimental results are presented to showcase: 1) best-fit ML model classifier; 2) performance of different tradeoffs in checkpointing; and 3) effectiveness of the proposed smart checkpointing scheme.
机译:完全可编程阀阵列(FPVA)是类似于VLSI现场可编程门阵列(FPGA)的通用可编程基于流量的微流体平台。 FPVA是动态可重构的,因此适用于涉及免疫测定和细胞分析的广泛应用。由于这些应用程序对安全至关重要,因此解决安全问题对于FPVA的成功和采用至关重要。这项研究评估了FPVA生物芯片的安全性。我们表明,FPVA容易受到类似于数字和基于流的微流体生物芯片的恶意操作的攻击。 FPVA进一步容易受到新型的攻击隧道攻击和故意老化。这项研究建立了安全性指标,并描述了对现实生活中生物测定法的可能攻击。此外,我们研究了基于黄金和实时生物芯片状态的机器学习(ML)技术对攻击的检测和分类。为了提高分类器的性能,我们提出了一种智能检查点机制。实验结果表明:1)最佳拟合机器学习模型分类器; 2)在检查点中执行不同的权衡; 3)提出的智能检查点方案的有效性。

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