Input-Layer Starvation: Why Per-Layer Pruning Breaks IoT Intrusion Detectors
Intrusion detectors for small Internet-of-Things (IoT) devices are usually compressed by pruning and judged by overall accuracy. We show that this hides a severe class-level failure, find its cause, and give low-overhead prevention and repair. On CICIoT2023, a two-layer convolutional detector pruned with uniform layer-wise magnitude pruning at 80% sparsity loses 16 points of accuracy but half of its macro-F1, the mean per-class F1 (0.542 to 0.271 over five independently trained models); 17 of 34 classes are materially damaged. Remaining weight count does not explain it: a perceptron and a transformer pruned to the same or fewer weights lose at most 0.096. The first layer does. It has 192 ...
| Authors | Md Anas Biswas |
|---|---|
| Primary category | cs.CR |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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