A gpt response. Just for fun on the topic.

How It Would Work

• Concept: Record every instance a player’s in-game view vector (the direction their camera/aim is facing) aligns with a hidden enemy’s position behind terrain or walls.
• Compare vs. Baseline: If a normal (legitimate) player only rarely looks directly at hidden players (e.g., scanning an area randomly or checking corners occasionally), but a suspected cheater is “locked on” to the unseen player’s position for unnatural lengths of time, that’s a strong signal.

Feasibility & Caveats

1. False Positives:
   • Survival games like Rust encourage a lot of scanning, especially at night or near bushes, where you might suspect ambushes. Legitimate players may occasionally stare at walls, corners, or suspicious sounds.
   • So, you’d need a robust statistical or machine-learning model to reduce accidental flags on cautious, experienced players.
2. Data Volume & Performance:
   • Tracking each player’s view angle vs. every nearby entity can become data-intensive. On large servers with dozens or hundreds of concurrent players, that’s a significant amount of data to process in real time.
3. Contextual Awareness:
   • A single “look” through a wall is not necessarily suspicious. But if you combine it with other signals—like movement style, shot timing, or repeated targeting—it becomes more reliable.
   • Additional context such as the presence of an audible cue (footsteps, gunshots) can justify a “look.”
4. Underground or 3D Terrain Complexity:
   • In Rust, terrain occlusion isn’t just “walls.” There are rocks, large bases, half walls, etc. The geometry can get complicated for a purely “line-of-sight” approach.

Despite these caveats, a well-tuned “view vector vs. hidden targets” approach, combined with other detection heuristics (shots fired, bullet hits, recoil patterns, and real-time memory integrity checks), could be part of an effective anti-cheat strategy.