DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs

arXiv:2403.13563v1 Announce Type: cross
Abstract: 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 …

abstract arxiv chips cs.ar cs.cr cs.lg deep learning detection flooding framework fusion localization network novel rate scale service study type