189 lines
4.2 KiB
Markdown
Executable File
189 lines
4.2 KiB
Markdown
Executable File
# OSCam with AI Fake DCW Detector Test
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## Overview
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This repository contains a modified version of **OSCam** enhanced with an advanced **AI-inspired Fake DCW Detection and Voting System**.
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The goal of this system is to improve stability and reliability when multiple Control Word (CW) sources are available, such as:
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- Local readers
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- CacheEx peers
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- CSP sources
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- Virtual readers
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Instead of accepting the first CW received, this implementation collects multiple candidates and selects the most reliable one using weighted voting logic.
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---
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# 🔍 Problem It Solves
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In multi-reader or CacheEx environments, fake or unstable DCWs can appear.
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Default behavior:
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- First CW wins
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- Possible glitches, freezing, or unstable decoding
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With AI Fake DCW Detector:
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- Multiple CWs are collected
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- Sources are tracked
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- Votes are counted
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- Local readers can be weighted higher
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- Majority or timeout logic is applied
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- A reliable CW is selected
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Result:
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✔ Reduced fake DCWs
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✔ Increased decoding stability
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✔ Better CacheEx reliability
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✔ Smarter CW selection
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---
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# 🧠 Core Functions
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## 1️⃣ `cw_vote_add()`
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This function is called whenever a new CW is received.
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It:
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- Compares the CW with existing candidates
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- Increases vote count if it matches
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- Adds a new candidate if it is unique
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- Tracks:
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- Total votes
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- Local votes
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- Reader source
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- CW value
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- Identifies whether the source is:
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- Local reader
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- Virtual reader
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- CacheEx client
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- CSP source
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Each CW candidate is stored in a voting pool.
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---
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## 2️⃣ `cw_vote_decide()`
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This function determines the final CW to use.
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### Decision Process
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1. Count total votes
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2. Verify minimum vote requirement
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3. Calculate effective score:
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4. Check for majority (> 50%)
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5. Apply timeout logic
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6. Apply fallback strategy if necessary
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If a winner is found:
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- CW is copied into `er->cw`
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- CacheEx hit statistics are updated
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- Optional logging is performed
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If no clear winner exists, the system waits for more votes unless timeout rules apply.
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---
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# ⚙ Configuration Options
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| Parameter | Description |
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|------------|------------|
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| `cwvote_enabled` | Enable/disable voting system |
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| `cwvote_max_candidates` | Maximum CW candidates stored |
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| `cwvote_compare_len` | Number of bytes used for CW comparison |
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| `cwvote_local_weight` | Weight multiplier for local votes |
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| `cwvote_min_votes` | Minimum required votes before decision |
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| `cwvote_timeout` | Timeout in milliseconds |
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| `cwvote_fallback` | Fallback mode (1 = best candidate, 2 = first candidate) |
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| `cwvote_log_enabled` | Enable detailed debug logging |
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---
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# 🏆 Winner Selection Logic
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A CW is selected if:
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- It has a strict majority (> 50%)
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OR
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- Timeout is reached and fallback mode allows selection
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If timeout occurs and:
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- `fallback = 1` → best scoring candidate is selected
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- `fallback = 2` → first candidate (slot 0) is selected
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---
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# 📊 Intelligent Source Awareness
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The system distinguishes between:
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- Trusted local readers
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- Virtual readers
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- CacheEx clients
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- CSP sources
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Local readers can be weighted higher using `cwvote_local_weight`, improving reliability in hybrid environments.
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---
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# 📈 CacheEx Integration
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If the winning CW originates from CacheEx:
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- `cwcacheexhit` counters are incremented
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- Client and account statistics are updated
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- Global hit statistics are updated
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This improves monitoring accuracy and performance analysis.
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---
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# 🚀 Advantages
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- Detects and filters fake DCWs
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- Improves stability in multi-reader setups
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- Reduces glitching and freezing
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- Configurable and flexible logic
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- Seamlessly integrated into OSCam core
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---
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# 🧩 AI Concept
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Although not based on machine learning, this system applies AI-style decision logic:
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- Aggregates multiple inputs
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- Applies weighted scoring
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- Uses majority validation
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- Implements adaptive timeout fallback
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It behaves like deterministic intelligence for CW selection.
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---
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# Intended Use
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Designed for:
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- Emulated environments
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- CacheEx-heavy setups
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- Multi-reader configurations
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- Advanced OSCam tuning
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- High-stability streaming environments
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---
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# License
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Based on the OSCam open-source project.
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This repository includes custom modifications implementing advanced CW voting and fake DCW detection logic.
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