Pack Defender: Proactive Defense Against Packet Attacks in NoCs Using an XGBoost-RNN Model

The Network-on-Chip (NoC) serves as the critical communication backbone in modern Multi-Processor Systems-on-Chip (MPSoCs), particularly for Deep Learning (DL) hardware where it underpins the reliable execution of machine learning models by facilitating efficient data and weight exchange. However, the NoC is vulnerable to stealthy packet-based attacks initiated by malicious Intellectual Property (IP) cores. Such attacks can severely degrade NoC latency and throughput, which are critical for efficient DL inference, and even compromise the correctness of model execution. Current detection methods are inherently reactive; they identify anomalies by monitoring global system features only after an attack has manifested, lacking the foresight to anticipate impending threats. To address this, we propose Pack Defender, a proactive NoC security framework based on temporal behavior modeling that forecasts future system states and reuses its partial prediction generative model for detection, eliminating the need for a separate module. Experimental results show strong predictive power, with average/top-three similarities of 83%/92% for Source-Level Packet Dropping (SLPD) and 90%/94% for In - Network Packet Diversion (INPD). The low Mean Absolute Error (0.05 for SLPD, 0.03 for INPD) further confirms its accuracy. The detection model (XGBoost) achieves 100% accuracy, with recall rates of 96% and 99% for SLPD and INPD respectively, significantly outperforming state-of-the-art methods lacking proactive prediction.