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arXiv 2108.13307cs.CR
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基于神经网络的片上系统(SoC)安全性

Security For System-On-Chip (SoC) Using Neural Networks

Vedant Ghodke, Shubham Deshmukh, Atharva Deshpande, Ninad Ekbote, Swati Shilaskar

摘要:

随着嵌入式系统的发展,超大规模集成电路设计的复杂性和成本因素在全球范围内逐步增加,并已成为外包。现代计算IC现在使用片上系统来实现更好的片上处理和通信。在物联网(IoT)时代,安全性已成为片上系统(SoC)最关键的部分之一。恶意活动会产生异常的流量模式,影响系统的运行及其性能,这在计算量巨大的世界中是无法承受的。SoC有可能出现功能故障、信息泄漏,甚至拒绝服务(DoS)、硬件特洛伊木马以及其他许多被归类为安全威胁的因素。在本文中,我们的目的是比较和描述不同类型的恶意安全威胁,以及如何使用神经网络来防止这些攻击。脉冲神经网络(SNN)、尖峰神经结构(RTNA)是防止SoC受到攻击的神经网络的一部分。最后,还强调了SoC安全的发展趋势。

英文摘要:

With the growth of embedded systems, VLSI design phases complexity and cost factors across the globe and has become outsourced. Modern computing ICs are now using system-on-chip for better on-chip processing and communication. In the era of Internet-of-Things (IoT), security has become one of the most crucial parts of a System-on-Chip (SoC). Malicious activities generate abnormal traffic patterns which affect the operation of the system and its performance which cannot be afforded in a computation hungry world. SoCs have a chance of functionality failure, leakage of information, even a denial of services (DoS), Hardware Trojan Horses and many more factors which are categorized as security threats. In this paper, we aim to compare and describe different types of malicious security threats and how neural networks can be used to prevent those attacks. Spiking Neural Networks (SNN), Runtime Neural Architecture (RTNA) are some of the neural networks which prevent SoCs from attacks. Finally, the development trends in SoC security are also highlighted.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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