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：本研究针对片上网络提出了一种精细化的、注入率可调的洪泛式拒绝服务攻击模型，更重要的是，提出了DL2Fence这一新颖框架，该框架利用深度学习和帧融合技术进行DoS攻击的检测与定位。研究开发了分别用于分类和分割的两个卷积神经网络模型，以实现DoS攻击的检测和定位。在一个16x16的Mesh结构片上网络中，该框架的检测与定位准确率分别达到95.8%和91.7%，精确率分别达到98.5%和99.3%。当网络规模从8x8扩展到16x16时，该框架的硬件开销显著降低了76.3%，并且与现有最先进技术相比，所需硬件资源减少了42.4%。这一进展证明了DL2Fence在平衡大规模片上网络中卓越的检测性能与极低硬件开销方面的有效性。