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.

翻译：本研究提出了一种面向片上网络（NoCs）的可调节注入速率的精细洪泛拒绝服务（DoS）模型，并重点提出了DL2Fence——一种采用深度学习（DL）与帧融合（2F）技术的新型DoS检测与定位框架。我们分别开发了用于分类与分割的两个卷积神经网络模型，分别实现DoS攻击检测与定位。该框架在16x16网格NoC中实现了95.8%的检测准确率与91.7%的定位准确率，检测精度与定位精度分别达到98.5%和99.3%。从8x8扩展至16x16的NoC时，框架硬件开销显著降低76.3%，相比现有先进方案减少42.4%的硬件需求。该进展证明了DL2Fence在大型NoC中实现卓越检测性能与极低硬件开销之间平衡的有效性。