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Instead of instant bans, the system generates "flags." Once a threshold of flags is reached (e.g., repeated "Jesus" or "NoFall" flags), a punishment is triggered.

Monitoring incoming data for illegal rotation values or impossible interaction timings. 3. Detection Methodologies

To create a low-overhead detection suite that balances security with system stability.

Exploring the use of ML to identify subtle cheating patterns that evade traditional heuristic checks. 6. Conclusion

Mitigating issues caused by network lag or VR users.

Instead of instant bans, the system generates "flags." Once a threshold of flags is reached (e.g., repeated "Jesus" or "NoFall" flags), a punishment is triggered.

Monitoring incoming data for illegal rotation values or impossible interaction timings. 3. Detection Methodologies

To create a low-overhead detection suite that balances security with system stability.

Exploring the use of ML to identify subtle cheating patterns that evade traditional heuristic checks. 6. Conclusion

Mitigating issues caused by network lag or VR users.