Network-on-Chip (NoC) is widely used to facilitate communication between components in sophisticated System-on-Chip (SoC) designs. Security of the on-chip communication is crucial because exploiting any vulnerability in shared NoC would be a goldmine for an attacker that puts the entire computing infrastructure at risk. NoC security relies on effective countermeasures against diverse attacks, including attacks on anonymity. We investigate the security strength of existing anonymous routing protocols in NoC architectures. Specifically, this paper makes two important contributions. We show that the existing anonymous routing is vulnerable to machine learning (ML) based flow correlation attacks on NoCs. We propose lightweight anonymous routing with traffic obfuscation techniques to defend against ML-based flow correlation attacks. Experimental studies using both real and synthetic traffic reveal that our proposed attack is successful against state-of-the-art anonymous routing in NoC architectures with high accuracy (up to 99%) for diverse traffic patterns, while our lightweight countermeasure can defend against ML-based attacks with minor hardware and performance overhead.

翻译：片上网络（NoC）广泛应用于复杂片上系统（SoC）设计中组件间的通信。片上通信的安全性至关重要，因为利用共享NoC中的任何漏洞都将为攻击者提供"金矿"，使整个计算基础设施面临风险。NoC安全性依赖于针对多种攻击（包括匿名性攻击）的有效防御措施。本文研究了现有NoC架构中匿名路由协议的安全强度，并作出两项重要贡献：首先证明现有匿名路由易受基于机器学习（ML）的NoC流关联攻击；其次提出融合流量混淆技术的轻量级匿名路由方案以防御此类攻击。基于真实流量与合成流量的实验研究表明：我们提出的攻击方案能以极高准确率（高达99%）成功破解NoC架构中现有最先进的匿名路由，适用于多种流量模式；而所提出的轻量级防御措施仅需极小的硬件开销与性能代价即可有效抵御基于ML的攻击。