The Value of Spike Timing: A Leakage-Resistant Benchmark of SNN Design Choices for Network Intrusion Detection
Spiking neural networks (SNNs) are increasingly studied for network intrusion detection, but comparative evidence on how neuron models and spike encodings affect performance remains limited. Evaluation choices can further influence the observed results when preprocessing, capture structure, or scenario information crosses the train-test boundary. We evaluate nine snnTorch neuron families with three spike encodings, yielding 27 SNN configurations across four intrusion-detection benchmarks. We first screen the complete design space and then repeat the evaluation using train-only transforms, seed-independent partitions, and capture- or scenario-aware separation. At the shared operating point...
| Authors | Raj Patel, Shaswata Mitra, David Amebley et al. |
|---|---|
| Primary category | cs.CR |
| Published | 31 May 2026 |
| Last updated at source | 9 October 2026 |
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