Cheating in first-person shooters (FPS) has reached critical levels, damaging competitive integrity and driving players away. The proliferation of software-based cheats (aimbots, wallhacks, recoil scripts) and hardware-based exploits (DMA devices, controller injection, dual-PC setups) makes traditional anti-cheat methods ineffective. Signature-based detection and kernel monitoring cannot keep pace with rapidly evolving hacks.
This whitepaper proposes a behavioral analytics framework leveraging machine learning (ML) and anomaly detection, supported by cloud-native infrastructure such as AWS DynamoDB. Instead of trying to detect cheat software directly, the system identifies statistical anomalies in gameplay behavior—performance patterns that fall outside the natural limits of human skill.
The system establishes skill-tiered baselines across accuracy, engagement distance, reaction time, hitbox targeting, and behavioral patterns. By analyzing deviations, it detects cheaters with high confidence while minimizing false positives for legitimately skilled players.
Key benefits:
- Adaptability – Detects novel cheats without requiring signatures.
- Scalability – Cloud-based infrastructure supports millions of players.
- Fairness – Skill-based baselines prevent wrongful bans of elite players.
- Resilience – Resistant to obfuscation and bypass techniques.
Target audience: publishers such as Riot Games, Activision Blizzard, EA, Ubisoft, Bungie, Treyarch, Infinity Ward, Raven Software, DICE, and Blizzard Entertainment.
Modern anti-cheat systems rely on:
- Signature Detection – Identifying known cheat processes.
- Memory Scanning – Detecting unauthorized injections.
- Kernel-Level Monitoring – Blocking low-level manipulation.
- Reactive: Only works for known cheats. New hacks bypass until patched.
- Obfuscation: Cheats use encryption and virtualization to evade detection.
- Hardware Hacks: DMA devices and external PCs bypass software entirely.
- Arms Race: Developers remain one step behind hackers.
Result: FPS titles become unplayable at scale. Players lose trust, retention drops, and studios suffer brand damage.
Instead of chasing cheats directly, this system detects the impossible statistical fingerprints of cheating behavior. Even the best cheat cannot fully simulate the natural variance of human play.
Establish population-wide baselines across skill tiers using telemetry. Metrics include:
-
Combat Performance
- Engagement distance by weapon type
- Accuracy rates
- Hitbox distribution (head/torso/limbs)
- Time-to-kill (TTK)
- Reaction time to target visibility
-
Behavioral Patterns
- Movement routes and speed
- Reload frequency
- Weapon switching behavior
- Peek timing and cover usage
- Jump frequency and tactical context
-
Account Metrics
- Account age
- Match history and progression trends
- Player report frequency
Separate baselines for:
- Elite / Pro (Top 5%)
- Advanced (70–95th percentile)
- Average (30–70th percentile)
- Developing (Bottom 30%)
This prevents misclassifying legitimately skilled players.
Use ML models (isolation forests, autoencoders, ensembles) to detect multi-metric anomalies. Cheaters typically exhibit impossible combinations—e.g., perfect accuracy + subhuman reaction times + extended engagement ranges.
- Average Player: 100m distance, 50% accuracy, 50% headshots.
- Elite Player: 150m distance, 75% accuracy, 70% headshots.
- Cheater: 300m distance, 95% accuracy, 80% headshots.
- Average Player: 50m distance, 40% accuracy, 30% headshots.
- Elite Player: 75m distance, 80% accuracy, 40% headshots.
- Cheater: 175m distance, 80% accuracy, 60% headshots.
- Average Player: 10m distance, 80% accuracy, 20% headshots.
- Elite Player: 25m distance, 90% accuracy, 40% headshots.
- Cheater: 75m distance, 90% accuracy, 40% headshots.
Pattern: Impossible ranges, perfect consistency, unnatural precision.
Server-side telemetry ensures tamper-proof capture:
- Shots fired (timestamp, position, aim vector, weapon type)
- Hits registered (target, hitbox, distance)
- Player movement samples (x, y, z, velocity, jump events)
- Engagement lifecycle (spot → engage → kill/death)
- Contextual environment data (visibility, cover, lighting)
Chosen for:
- Scalability: Auto handles millions of events.
- Performance: Millisecond latency.
- Flexibility: NoSQL schema supports evolving metrics.
- Integration: Works with Lambda, Kinesis, S3, SageMaker.
Example Table: PlayerCombatEvents
| Primary Key | Attributes |
|---|---|
| PlayerID + Timestamp | GameID, MatchID, WeaponType, EventType, TargetID, Distance, Hitbox, Position, AimVector, Velocity, TTK, ReactionTime |
Global Secondary Index: SkillTierAnalysis for tier-based querying.
Aggregate metrics by player/weapon/session:
- Avg engagement distance
- Accuracy %
- Headshot %
- Avg TTK
- Avg reaction time
Compute z-scores:
Z = (PlayerMetric – BaselineMean) / BaselineStdDev
AnomalyScore = Σ(Weight[M] × |Z[M]|)
Boost score if impossible combinations detected (e.g., accuracy > 90% + reaction < 150ms + headshot > 70%).
- Green: Anomaly < 3.0 → No action
- Yellow: 3.0–5.0 → Monitor
- Red: ≥ 5.0 → Review/ban
- Features: Temporal patterns, metric correlations, consistency.
- Models: Isolation forests (unsupervised), Random Forests (classification), LSTMs (time-series).
- Continuous training on new cheat data.
- Require multi-metric anomalies, not single spikes.
- Track over time windows, not isolated sessions.
- Profile playstyles (routes, reloads, timing).
- Manual review + transparent appeal process.
- Phase 1 – Infrastructure setup (DynamoDB, telemetry, pipelines).
- Phase 2 – Baseline establishment with population data.
- Phase 3 – Deploy anomaly detection + review workflows.
- Phase 4 – ML model integration and adaptive detection.
- Phase 5 – Continuous monitoring, feedback loops, updates.
- Adaptive: No signatures needed.
- Comprehensive: Detects software & hardware cheats.
- Bypass-resistant: Evades obfuscation tactics.
- Scalable: Cloud-native, millions of players supported.
- Evidence-based: Provides statistical proof.
- Player-trust focused: Reduces false bans, transparent.
Initial Costs:
- Cloud infra: $50k–100k setup
- Dev resources: 6–12 engineer-months
- Data science: 3–6 months
Ongoing Costs:
- DynamoDB + compute: $8k–30k/month (scale-based)
- Review team: variable
Benefits:
- Increased retention and engagement
- Reduced churn from cheaters
- Competitive brand advantage
- Long-term cost savings in support
Payback: 6–12 months for AAA titles.
This blueprint is intended as open collaboration:
- Open-source implementations
- Shared anonymized datasets
- Standardized metrics across studios
- Joint research initiatives
Studios like Riot, Activision, EA, Ubisoft, and Bungie must commit to implementation. The technology exists, the methodology is proven—what’s needed is execution.
Cheating is not just a technical issue—it is the greatest existential threat to competitive FPS gaming. Signature-based anti-cheat is obsolete. The future is behavioral analytics powered by ML, scalable cloud infrastructure, and community-driven innovation.
By establishing adaptive baselines, analyzing anomalies, and enforcing fair play, we can restore competitive integrity to FPS games and rebuild player trust.
Cheating kills games. Data-driven anti-cheat saves them.
ZeroBandwidth
Community Anti-Cheat Advocate