GNS: <u>G</u>raph-Based <u>N</u>etwork-on-Chip <u>S</u>hield for Early Defense Against Malicious Nodes in MPSoC

In the rapidly evolving landscape of system design, Multi-Processor Systems-on-Chip (MPSoCs) have experienced significant growth in both scale and complexity, by integrating an array of Intellectual Properties (IPs) through Network-on-Chip (NoC) to execute complex parallel applications. However, this advancement has led to the emergence of security attacks caused by Malicious Third-Party IPs (M3PIPs), such as Denial-of-Service (DoS). Many current methods for detecting DoS attacks involve significant hardware overhead and are often inefficient in identifying anomalies at an early stage. Addressing this gap, we propose the Graph-based NoC Shield (GNS), a robust strategy meticulously crafted to detect, localize, and isolate malicious IPs at the very early stage of DoS appearance. Central to our approach is the use of a Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) detection model. This combination capitalizes on network traffic data and routing dependency graphs to efficiently trace the source of network congestion and pinpoint attackers. Our extensive experimental analysis validates the effectiveness of the GNS framework, demonstrating a 98% detection accuracy and localization capabilities, achieved with minimal hardware overhead of 1.8% in each router, based on a pure 4*4 Mesh NoC system. The detection performance exceeds that of all other state-of-the-art works and most straightforward single machine learning inference models within the same context. Additionally, the hardware overhead is notably superior compared to other security schemes. Another key feature of our system is the implementation of a credit interposing mechanism. It was specifically designed to isolate M3PIPs engaging in Flooding-based DoS and effectively mitigate the spread of malicious traffic. This approach significantly enhances the security of NoC-based MPSoCs, offering early-stage detection with the superior accuracy compared to other models. Crucially, the GNS achieves this with up to 75% less hardware overhead than state-of-the-art solutions, thus striking a balance between efficiency and effectiveness in security implementation.