Network-on-Chip (NoC) is widely used as the internal communication fabric in today's multicore 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. NoC security relies on effective countermeasures against diverse attacks. 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 a lightweight anonymous routing that use traffic obfuscation techniques which can 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 a 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）作为现代多核片上系统（SoC）内部通信基础设施被广泛采用。芯片间通信的安全性至关重要，因为利用共享NoC中的任何漏洞都可能成为攻击者的“金矿”。NoC的安全性依赖于针对多种攻击的有效防御措施。我们研究了NoC架构中现有匿名路由协议的安全强度。具体而言，本文做出两项重要贡献：首先，我们证明现有匿名路由在NoC上容易受到基于机器学习（ML）的流关联攻击；其次，我们提出一种轻量级匿名路由方案，利用流量混淆技术抵御基于ML的流关联攻击。基于真实流量与合成流量的实验研究表明：我们提出的攻击能以高准确率（针对多种流量模式高达99%）成功破解NoC架构中最先进的匿名路由方案，而所提出的轻量级防御机制仅需极低的硬件开销和性能代价即可抵御基于ML的攻击。