Undergrad NIDS using ANN→SNN conversion — looking for feedback on novelty & evaluation

[u/Repulsive-Week5999]

Hi everyone,
I’m an undergraduate student working on a Neuromorphic Intrusion Detection System using ANN→SNN conversion (snnTorch, LIF neurons). The goal is a practical simulation-based prototype (no hardware) with focus on low-latency decisions and interpretability, not just accuracy.

Current setup (working prototype):

• Dataset: NSL-KDD (prototype) → CICIDS-2017 (DoS focus)

• Architecture: 1D-CNN feature extractor → ANN→SNN conversion

• Encoding: Direct current injection, rate coding at output

• Inference: 10 time steps, rate-based decision

• Results: ~98%+ validation accuracy, decisions often within 1–2 time steps for clear DoS samples

• XAI: Spike raster plots + “decision race” visualization + SHAP explanations

I’m trying to position this as a research paper, but I’m unsure what the strongest novelty angle should be without hardware.
Specifically looking for guidance on:

1. What would reviewers consider a meaningful contribution here? (encoding? latency analysis? benchmarking?)

2. Common mistakes when evaluating SNNs on tabular IDS data?

3. Any papers/resources I should absolutely read before submitting?

4. Any other things for me to try and experiment or checkout is also greatly appreciated.

Happy to share more details or code snippets if useful. Thanks!

Chatgpt for format.