Set up anti-bots and behavioral analysis on the platform
Introduction
Bots and automated scripts compromise the integrity of games and platform security. Comprehensive anti-boot protection combines behavioral analysis on the client, scoring of anomalies on the backend and forced checks (CAPTCHA, 2FA), providing minimal impact on UX with maximum detection accuracy.
1. Threats and attack scenarios
Credential stuffing and brute-force: massive login attempts from the bot network.
Automated spins: Scripts that emulate WebSocket requests to spin to bypass speed limits.
Price-separate-sessions (session hijacking): theft of tokens and automatic betting.
Bots for bonus pharma: mass registration under fake accounts and extraction of bonuses.
2. Client detection mechanisms
1. Browser fingerprinting
Fingerprint collection (Canvas, WebGL, plugins, timezone, list of MIME-types) via FingerprintJS.
Comparison with the base of known bot patterns and speed-run fingerprint changes.
2. JavaScript Challenges (Proof-of-Work)
Small computational tasks before critical actions (login, spin) with nonce return.
Holds bots with simple HTTP clients without a JS engine.
3. Canvas blocking and WebSocket analysis
Checking the ability to render animations and support for the WebSocket API.
Emulate delays and heartbeat messages in the client.
3. Reactive checks and CAPTCHA flow
Adaptive CAPTCHA
Trigger conditions: too frequent clicks on the "Spin" button, exceeding QPS, suspicious IP.
Using invisible reCAPTCHA v3 with threshold scoring; if score <0. 5 → showing the classic reCAPTCHA v2.
2FA-input at output
For suspicious activity, requires an SMS code or OTP email before payout.
Device challenge
When changing a device or geo-location, the platform requires a driver's license through the KYC API.
4. Backend scoring behavior
1. Telemetry Collection
Kafka-topics: 'user. events` (clicks, pageViews, apiCalls, socketEvents).
Storage at ClickHouse for real-time and historical analysis.
2. Feature engineering
Time signs: Δ t between clicks, average rate of bets.
Spatial: changes to IP/Subnet, User-Agent flips.
Sessions: length, depth of navigation, flow patterns.
3. ML model of anomalies
Isolation Forest and Autoencoder for scoring each session.
Threshold separation: low/medium/high risk → mapping to measures (warning, CAPTCHA, blocking).
5. SIEM and SOC integration
Logstash/Fluentd takes away the logs of anti-bots and behavioral scoring.
Elastic SIEM or Splunk: incident correlation, bot attack dashboards and false-positive rate.
Automated alerts
PagerDuty announcements with a surge of suspicious sessions> X% of active ones.
Slack notifications to the SOC team.
6. Microservices Architecture
```mermaid
flowchart LR
subgraph Client
Browser
end
subgraph Platform
API-Gateway
AuthService
AntiBotClientService
BehaviorScoringService
CAPTCHASvc
TransactionService
SIEMConnector
end
AntiBotClientService: checking JS challenges and fingerprint.
BehaviorScoringService: aggregates events and issues a riskScore.
CAPTCHASvc-Manages the adaptive CAPTCHA API.
SIEMConnector: sends incidents to SIEM.
7. Testing and debugging
Synthetic bots: Puppeteer/Playwright scripts for emulating attacks.
A/B tests: evaluation of UX-impact: conversion rate before and after protection implementation.
False-positive analysis: manual verification of medium-risk cases, ML-model adjustment.
Conclusion
Effective anti-boot protection on the online casino platform combines client-side fingerprinting and PoW challenges, adaptive CAPTCHA and 2FA, and on the backend, ML-based behavioral scoring and SIEM integration. This layered approach balances user-experience with strong protection against automated attacks.
Bots and automated scripts compromise the integrity of games and platform security. Comprehensive anti-boot protection combines behavioral analysis on the client, scoring of anomalies on the backend and forced checks (CAPTCHA, 2FA), providing minimal impact on UX with maximum detection accuracy.
1. Threats and attack scenarios
Credential stuffing and brute-force: massive login attempts from the bot network.
Automated spins: Scripts that emulate WebSocket requests to spin to bypass speed limits.
Price-separate-sessions (session hijacking): theft of tokens and automatic betting.
Bots for bonus pharma: mass registration under fake accounts and extraction of bonuses.
2. Client detection mechanisms
1. Browser fingerprinting
Fingerprint collection (Canvas, WebGL, plugins, timezone, list of MIME-types) via FingerprintJS.
Comparison with the base of known bot patterns and speed-run fingerprint changes.
2. JavaScript Challenges (Proof-of-Work)
Small computational tasks before critical actions (login, spin) with nonce return.
Holds bots with simple HTTP clients without a JS engine.
3. Canvas blocking and WebSocket analysis
Checking the ability to render animations and support for the WebSocket API.
Emulate delays and heartbeat messages in the client.
3. Reactive checks and CAPTCHA flow
Adaptive CAPTCHA
Trigger conditions: too frequent clicks on the "Spin" button, exceeding QPS, suspicious IP.
Using invisible reCAPTCHA v3 with threshold scoring; if score <0. 5 → showing the classic reCAPTCHA v2.
2FA-input at output
For suspicious activity, requires an SMS code or OTP email before payout.
Device challenge
When changing a device or geo-location, the platform requires a driver's license through the KYC API.
4. Backend scoring behavior
1. Telemetry Collection
Kafka-topics: 'user. events` (clicks, pageViews, apiCalls, socketEvents).
Storage at ClickHouse for real-time and historical analysis.
2. Feature engineering
Time signs: Δ t between clicks, average rate of bets.
Spatial: changes to IP/Subnet, User-Agent flips.
Sessions: length, depth of navigation, flow patterns.
3. ML model of anomalies
Isolation Forest and Autoencoder for scoring each session.
Threshold separation: low/medium/high risk → mapping to measures (warning, CAPTCHA, blocking).
5. SIEM and SOC integration
Logstash/Fluentd takes away the logs of anti-bots and behavioral scoring.
Elastic SIEM or Splunk: incident correlation, bot attack dashboards and false-positive rate.
Automated alerts
PagerDuty announcements with a surge of suspicious sessions> X% of active ones.
Slack notifications to the SOC team.
6. Microservices Architecture
```mermaid
flowchart LR
subgraph Client
Browser
end
subgraph Platform
API-Gateway
AuthService
AntiBotClientService
BehaviorScoringService
CAPTCHASvc
TransactionService
SIEMConnector
end
| Browser --> | events | API-Gateway |
|---|---|---|
| API-Gateway --> AntiBotClientService | ||
| AntiBotClientService --> | fingerprint, PoW | BehaviorScoringService |
| BehaviorScoringService --> | riskScore | API-Gateway |
| API-Gateway --> | challenge | CAPTCHASvc |
| API-Gateway --> TransactionService | ||
| BehaviorScoringService --> SIEMConnector | ||
| ``` |
AntiBotClientService: checking JS challenges and fingerprint.
BehaviorScoringService: aggregates events and issues a riskScore.
CAPTCHASvc-Manages the adaptive CAPTCHA API.
SIEMConnector: sends incidents to SIEM.
7. Testing and debugging
Synthetic bots: Puppeteer/Playwright scripts for emulating attacks.
A/B tests: evaluation of UX-impact: conversion rate before and after protection implementation.
False-positive analysis: manual verification of medium-risk cases, ML-model adjustment.
Conclusion
Effective anti-boot protection on the online casino platform combines client-side fingerprinting and PoW challenges, adaptive CAPTCHA and 2FA, and on the backend, ML-based behavioral scoring and SIEM integration. This layered approach balances user-experience with strong protection against automated attacks.
