ML-Assisted Attack Detection on NoC-Based Many-Cores Through On-Chip Traffic Monitoring

Multi-and many-core processors based on a network-on-chip (NoC) interconnect are pervasive in computing platforms ranging from server farms to embedded systems. Such complex systems often make wide use of third-party intellectual property elements from untrusted organizations. This manuscript proposes a methodology that combines on-chip traffic monitoring, through the insertion of lightweight counters in the NoC routers, and on-chip analysis, through machine-learning techniques, into a blue-team approach that detects the execution of unintended applications with an average accuracy of 89% and limited overheads in terms of area, power, performance, and timing.