arXiv：cs.AI· Raj Patel, Shaswata Mitra, David Amebley, Taye Akinrele, Sayanton Dibbo, Sudip Mittal, Shahram Rahimi·· 5 小时前
SNN 网络入侵检测设计选择的抗泄漏基准：脉冲时序的价值
The Value of Spike Timing: A Leakage-Resistant Benchmark of SNN Design Choices for Network Intrusion Detection
AI 导读
研究者用 snnTorch 的九类神经元与三种脉冲编码组合出 27 种 SNN 配置，在四个入侵检测基准上评估，并复测了仅训练集变换、与随机种子无关的划分及按抓包或场景分离的设定。
正文
Abstract: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, LeakyParallel/latency achieved the lowest mean rank in both sweeps but was not statistically separated from Leaky/latency; their screening order reversed when CTU-13 was excluded. Overall, screening and confirmation retained substantial rank agreement despite changes in measured performance.
We then examine what latency coding contributes by holding the active spike set fixed and changing only its temporal organization. At the main T=25 operating point, mapping feature magnitude to spike time improved macro-F1 on all five confirmation protocols, while spreading the same spikes through time without that mapping could either improve or reduce performance. Latency coding thus combines informative spike timing with temporal network dynamics. Sparse input encoding does not directly imply sparse internal activity, although latency coding requires fewer fanout-weighted operations than rate coding in the evaluated models. These findings motivate separate assessment of SNN design, evaluation protocol, spike timing, and computational activity for static network-flow data.
| Comments: | This manuscript has been accepted at IEEE CogMI 2026. \c{opyright} 2026 IEEE. Personal use is permitted; all other uses require IEEE permission, including reprinting, republication, redistribution, resale, or reuse of copyrighted components |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2606.01442 [cs.CR] |
| (or arXiv:2606.01442v3 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2606.01442 arXiv-issued DOI via DataCite |
Submission history
From: Raj Patel [view email]
[v1]
Sun, 31 May 2026 20:25:15 UTC (1,235 KB)
[v2]
Mon, 31 Aug 2026 17:10:02 UTC (325 KB)
[v3]
Fri, 9 Oct 2026 18:11:08 UTC (330 KB)
来源：arXiv：cs.AI · arxiv.org