DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs
This addresses the problem of efficient and low-overhead security in large-scale Network-on-Chips for hardware designers, though it appears incremental as it builds on existing deep learning and fusion techniques.
The study tackled the problem of detecting and localizing refined Denial-of-Service attacks in large-scale Network-on-Chips by introducing DL2Fence, a framework that achieved detection and localization accuracies of 95.8% and 91.7% with precision rates of 98.5% and 99.3% in a 16x16 mesh NoC, while reducing hardware overhead by 76.3% when scaling and requiring 42.4% less hardware compared to state-of-the-art methods.
This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8% and 91.7%, and precision rates of 98.5% and 99.3% in a 16x16 mesh NoC. The framework's hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence's effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.