Presentation
DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs
DescriptionThis 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 NoC. The framework's hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16, 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.
Event Type
Research Manuscript
TimeThursday, June 2711:06am - 11:24am PDT
Location3008, 3rd Floor
Security
Hardware Security: Primitives, Architecture, Design & Test