基于LUT编译的Kolmogorov-Arnold网络用于物联网边缘设备轻量级DoS检测
LUT-Compiled Kolmogorov-Arnold Networks for Lightweight DoS Detection on IoT Edge Devices
AI总结：
本文提出LUT编译的Kolmogorov-Arnold网络，用于在物联网边缘设备上实现轻量级拒绝服务攻击检测，通过预计算查找表显著降低计算开销，同时保持高检测精度。
摘要：
拒绝服务（DoS）攻击对物联网（IoT）生态系统构成了重大威胁，但如何在资源受限的边缘设备上部署有效的入侵检测仍具挑战性。Kolmogorov-Arnold网络（KANs）通过在边放置可学习的单变量样条函数而非在节点上使用固定激活函数，提供了一种比多层感知机（MLPs）更紧凑的替代方案，从而在更少的参数下实现竞争性的准确性。然而，运行时B样条评估引入了显著的计算开销，这在对延迟敏感的物联网应用中是不可接受的。我们提出了一种查找表（LUT）编译流水线，通过预计算的量化表和线性插值替代昂贵的样条计算，大幅降低了推理延迟，同时保持检测质量。我们的轻量级KAN模型（50K参数，0.19~MB）在CICIDS2017 DoS数据集上实现了99.0%的准确性。在LUT编译后，模型在分辨率L=8时保持98.96%的准确性（F1退化<0.0004），在批量大小为256时实现了68倍的加速，在批量大小为1时实现了超过5000倍的加速，仅需2倍的内存开销。我们对LUT分辨率、量化方案和越界策略进行了全面评估，确立了准确率-延迟-内存权衡的清晰帕累托前沿。我们的结果表明，LUT编译的KANs能够在仅使用CPU的物联网网关上实现实时的DoS检测，具有确定性的推理延迟和最小的资源占用。
英文摘要：
Denial-of-Service (DoS) attacks pose a critical threat to Internet of Things (IoT) ecosystems, yet deploying effective intrusion detection on resource-constrained edge devices remains challenging. Kolmogorov-Arnold Networks (KANs) offer a compact alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate spline functions on edges rather than fixed activations on nodes, achieving competitive accuracy with fewer parameters. However, runtime B-spline evaluation introduces significant computational overhead unsuitable for latency-critical IoT applications. We propose a lookup table (LUT) compilation pipeline that replaces expensive spline computations with precomputed quantized tables and linear interpolation, dramatically reducing inference latency while preserving detection quality. Our lightweight KAN model (50K parameters, 0.19~MB) achieves 99.0\% accuracy on the CICIDS2017 DoS dataset. After LUT compilation with resolution $L=8$, the model maintains 98.96\% accuracy (F1 degradation $<0.0004$) while achieving $\mathbf{68\times}$ speedup at batch size 256 and over $\mathbf{5000\times}$ speedup at batch size 1, with only $2\times$ memory overhead. We provide comprehensive evaluation across LUT resolutions, quantization schemes, and out-of-bounds policies, establishing clear Pareto frontiers for accuracy-latency-memory trade-offs. Our results demonstrate that LUT-compiled KANs enable real-time DoS detection on CPU-only IoT gateways with deterministic inference latency and minimal resource footprint.
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