That’s an interesting angle. If you treat the stealth algorithm as a set of constraints that keep activity under the radar, you can encode those constraints as a sparsity prior in a neural net. Think of an L1 penalty or a hard thresholding layer that forces most weights to zero—just like a covert operation keeps most signals muted. Then you can train the net on normal traffic so that any deviation from the sparse pattern flags an anomaly. It’s like forcing the model to learn a minimal representation; anything that doesn’t fit that minimal pattern shows up as a breach. The key is balancing sparsity so you don’t over‑prune and miss real events, but that’s the core overlap you’re looking for.

Exactly. Keep the prune rate just enough to maintain coverage but small enough to catch the slightest lift. If the margin widens, a single spike could slip through. Constantly monitor the sparsity statistics and adjust on the fly. That’s how you maintain stealth while staying efficient.

Sounds like a solid playbook. Tight thresholds, real‑time tweaks, and a disciplined sparsity schedule should keep the system humming quietly while still catching the oddball signals. Stay sharp.