用查表法加速KAN模型，实现在物联网设备上低延迟的拒绝服务攻击检测。
LUT-Compiled Kolmogorov-Arnold Networks for Lightweight DoS Detection on IoT Edge Devices
- 将可学习的样条函数替换为预计算的查表与线性插值，降低推理开销。
- 50K参数模型在CICIDS2017数据集上达99.0%准确率，查表后仅降0.04%。
- 在单批次下提速超5000倍，适合对延迟敏感的边缘设备部署。
拒绝服务（DoS）攻击对物联网生态系统构成严重威胁，但在资源受限的边缘设备上实现有效入侵检测仍具挑战。柯尔莫戈洛夫-阿诺德网络（KANs）通过在边而非节点上放置可学习的一元样条函数，相比多层感知机（MLPs）以更少参数达到相近精度。然而，运行时的B样条计算带来显著开销，不适用于对延迟敏感的物联网应用。本文提出一种查表（LUT）编译流程，将昂贵的样条运算替换为预计算的量化表格与线性插值，大幅降低推理延迟并保持检测质量。所提出的轻量级KAN模型（50K参数，0.19~1.98MB）在CICIDS2017 DoS数据集上取得99.0%准确率。经由分辨率L=8的LUT编译后，准确率维持在98.96%（F1下降<0.0004），批量大小256时提速68倍，批量大小1时超5000倍，内存仅增加2倍。我们全面评估了不同查表分辨率、量化方案及越界处理策略，建立了清晰的准确率-延迟-内存权衡边界。结果表明，经查表编译的KAN可实现在纯CPU物联网网关上的实时DoS检测，具备确定性延迟与极小资源占用。
原文摘要 · Abstract (English)
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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