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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. We investigate the security strength of existing anonymous routing protocols in NoC architectures, making two pivotal contributions. Firstly, we develop and perform a machine learning (ML)-based flow correlation attack on existing anonymous routing techniques in NoC systems, revealing that they provide only packet-level anonymity. Secondly, we propose a novel, lightweight anonymous routing protocol featuring outbound traffic tunneling and traffic obfuscation. This protocol is designed to provide robust defense against ML-based flow correlation attacks, ensuring both packet-level and flow-level anonymity. Experimental evaluation using both real and synthetic traffic demonstrates that our proposed attack successfully deanonymizes state-of-the-art anonymous routing in NoC architectures with high accuracy (up to 99%) for diverse traffic patterns. It also reveals that our lightweight anonymous routing protocol can defend against ML-based attacks with minor hardware and performance overhead.
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This content will become publicly available on November 12, 2027
Leveraging {Chameleon Cloud} for Hands-On Learning in Systems and Data Courses: An Experience Report
Large-scale hands-on computing education often requires infrastructure beyond what individual courses can provide. Research infrastructure can fill this gap. We present an experience report on using the [Anonymous Testbed] in four course offerings, organized into three cases: storage systems, data analysis and visualization, and machine learning systems engineering and operations. Across these cases, [Anonymous] supported realistic, reproducible, and flexible learning experiences, while also introducing operational challenges around workflow design, support, and scale. We conclude with practical lessons for instructors designing systems- and data-intensive courses around shared research infrastructure.
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- PAR ID:
- 10705750
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 979-8-4007-2506-7
- Subject(s) / Keyword(s):
- computing education, cloud computing, research infrastructure, computer systems education
- Format(s):
- Medium: X
- Location:
- Virtual
- Sponsoring Org:
- National Science Foundation
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