Design and Exploration of Mobile Game Anti‑Fraud Systems

Introduction – Mobile games have become an essential part of young people’s lives, and the black‑market industry surrounding them continuously evolves with new cheating methods and counter‑measures. The presentation is divided into five parts: the mobile game black‑market chain, common fraud forms, anti‑fraud system design, risk‑control effectiveness, and a Q&A session.

1. Mobile Game Black‑Market Chain – The ecosystem consists of three layers: upstream tool and script developers, mid‑stream agents who distribute these tools, and downstream consumers including illicit studios, regular players, and individual workshops. Players themselves can also be part of the chain, especially when streamers collaborate with cheat providers.

2. Common Fraud Forms in Mobile Games

• Content security abuses such as spammy nicknames and signatures. • Script and cheat distribution for FPS, SLG, and card games (e.g., aimbots, wall‑hacks, resource bots). • Account trading and initial‑account sales that give unfair advantages. • Illegal recharge (代充) and refund exploitation using cross‑border payment gaps. • Advertising fraud, “羊毛党” (coupon‑grabbing), and leaderboard manipulation.

3. Mobile Game Anti‑Fraud System Design – The architecture separates content‑security modules from core game modules and further splits each into real‑time and offline components. Early‑stage player actions (registration, tutorial, basic quests) are monitored closely because they present low‑cost attack surfaces. Real‑time checks evaluate each request’s risk, while offline analysis aggregates behavior across devices, IPs, and accounts to refine risk scores.

Risk mitigation adapts with player lifecycle: simple CAPTCHAs for newcomers, stricter verification (face ID, phone binding) for mid‑stage users, and graduated penalties (warnings → temporary bans → permanent bans) for repeat offenders. The system also supports multi‑region configurations to respect local regulations.

4. Risk‑Control Effectiveness and Reflections – Comprehensive profiling of players and entities enables both fraud detection and fine‑grained operational analytics. Strategies differ by region (e.g., Korean military‑style account sharing) and by game genre (FPS cheats vs. SLG resource farms). Machine‑learning models are employed for interpretability, and the design emphasizes low coupling between detection and enforcement modules to hide detection logic from adversaries.

5. Q&A Highlights

International KYC often relies on social‑login IDs (Google, Facebook) rather than national ID cards.

Typical anti‑fraud teams consist of 10‑20 specialists covering strategy, analysis, and engineering.

Recharge fraud and illegal iOS refunds represent a significant revenue risk, especially when black‑card or refund abuse is involved.

Unknown cheat types are detected by monitoring invariant steps (e.g., mandatory login before recharge) and flagging anomalous deviations.

Account‑trading detection combines on‑chain behavior analysis with external marketplace monitoring; penalties are calibrated to maintain a balance between security and player experience.

In summary, effective mobile game anti‑fraud requires a blend of real‑time risk scoring, offline data mining, adaptable regional policies, and tightly coupled yet decoupled detection‑enforcement pipelines.