SECTAR: Secure NoC using Trojan Aware Routing
SECTAR: Secure NoC using Trojan Aware Routing
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DOI：
10.1109/nocs50636.2020.9241711
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
M. Rajan;Abhijit Das;John Jose;P. Mishra
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文献类型：
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作者：
M. Rajan;Abhijit Das;John Jose;P. Mishra
片上系统 (SoC) 使用多个第三方供应商的不同知识产权 (IP) 模块进行设计，以降低设计成本，同时满足严格的上市时间限制。设计值得信赖的 SoC 需要解决与供应链安全漏洞相关的日益增长的担忧。 IP 上的恶意植入（例如硬件木马 (HT)）是设计值得信赖的 SoC 时面临的重大安全威胁之一。使用传统的硅前和硅后验证方法来检测复杂的多处理器 SoC 中的木马是一项重大挑战。基于数据包的片上网络 (NoC) 是一种广泛使用的解决方案，用于复杂 SoC 中 IP 之间的片上通信。本文的重点是在存在潜在不可信 IP 的情况下实现可信 NoC 通信。本文做出了三个关键贡献。 (1) 我们在 NoC 路由器中对 HT 进行建模，该路由器会激活数据包的错误路由以发起拒绝服务、服务延迟和注入抑制。 (2)我们提出了一种动态屏蔽技术，可以隔离已识别的HT感染IP。 (3) 我们提出了一种安全路由算法来绕过 HT 感染的 NoC 路由器。受 HT 感染的 NoC 的实验结果表明，所提出的方法在实际基准测试中有效平均数据包延迟降低了 38%，在合成流量模式中降低了 48%。我们的方法还提高了吞吐量，并将实际基准中的有效平均偏转数据包延迟减少了 62%，在合成流量模式中减少了 97%。
System-on-Chips (SoCs) are designed using different Intellectual Property (IP) blocks from multiple third-party vendors to reduce design cost while meeting aggressive time-to-market constraints. Designing trustworthy SoCs need to address the increasing concerns related to supply-chain security vulnerabilities. Malicious implants on IPs, such as Hardware Trojans (HTs) are one of the significant security threats in designing trustworthy SoCs. It is a major challenge to detect Trojans in complex multi-processor SoCs using conventional pre- and post-silicon validation methodologies. Packet-based Network-on-Chip (NoC) is a widely used solution for on-chip communication between IPs in complex SoCs. The focus of this paper is to enable trusted NoC communication in the presence of potentially untrusted IPs. This paper makes three key contributions. (1) We model an HT in NoC router that activates misrouting of the packets to initiate a denial of service, delay of service, and injection suppression. (2) We propose a dynamic shielding technique that isolates the identified HT infected IP. (3) We present a secure routing algorithm to bypass the HT infected NoC router. Experimental results on HT infected NoC demonstrate that the proposed method reduces effective average packet latency by 38% in real benchmarks and 48% in synthetic traffic patterns. Our method also increases throughput and reduces effective average deflected packet latency by 62% in real benchmarks and 97% in synthetic traffic patterns.