  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.
