DL2Fence: Integrating Deep Learning and Frame Fusion for Enhanced
  Detection and Localization of Refined Denial-of-Service in Large-Scale NoCs
Haoyu Wang Basel Halak Jianjie Ren Ahmad Atamli
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.