commit 080d46ea3d427ce080e7b4d22a573a7e88454fee
Author: acidvegas <acid.vegas@acid.vegas>
Date:   Mon Nov 25 22:28:06 2024 -0500

    Initial commit

diff --git a/README.md b/README.md
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+# PyLCG
+> Linear Congruential Generator for IP Sharding
+
+PyLCG is a Python implementation of a memory-efficient IP address sharding system using Linear Congruential Generators *(LCG)* for deterministic random number generation. This tool enables distributed scanning and network reconnaissance by efficiently dividing IP ranges across multiple machines.
+
+___
+
+## Table of Contents
+
+- [Project Origins & Purpose](#project-origins-and-purpose)
+- [Overview](#overview)
+- [How It Works](#how-it-works)
+    - [Understanding IP Addresses](#understanding-ip-addresses)
+    - [The Magic of Linear Congruential Generators](#the-magic-of-linear-congruential-generators)
+    - [Sharding: Dividing the Work](#sharding-dividing-the-work)
+    - [Memory-Efficient Processing](#memory-efficient-processing)
+- [Real-World Applications](#real-world-applications)
+    - [Network Security Testing](#network-security-testing)
+    - [Cloud-Based Scanning](#cloud-based-scanning)
+
+___
+
+## Project Origins & Purpose
+
+PyLCG was inspired by the elegant IP distribution system used in [masscan](https://github.com/robertdavidgraham/masscan), the popular mass IP port scanner. While masscan implements this logic as part of its larger codebase, I wanted to isolate and implement this specific component as a standalone Python library that developers can easily integrate into their own projects.
+
+The goal was to create a clean, well-documented implementation that:
+- Can be used as a drop-in solution for any project needing IP distribution capabilities
+- Provides the same reliable mathematical foundation as masscan's approach
+- Is easy to understand and modify for specific needs
+- Works well with modern Python async patterns
+
+By extracting this functionality into its own library, developers can add sophisticated IP distribution capabilities to their network tools without having to reinvent the wheel or extract code from larger projects.
+
+___
+
+## Overview
+
+When performing network reconnaissance or scanning large IP ranges, it's often necessary to split the work across multiple machines. However, this presents several challenges:
+
+1. You want to ensure each machine works on a different part of the network *(no overlap)*
+2. You want to avoid scanning IPs in sequence *(which can trigger security alerts)*
+3. You need a way to resume scans if a machine fails
+4. You can't load millions of IPs into memory at once
+
+PyLCG solves these challenges through clever mathematics and efficient algorithms.
+
+___
+
+## How It Works
+
+### Understanding IP Addresses
+
+First, let's understand how IP addresses work in our system:
+
+- An IP address like `192.168.1.1` is really just a 32-bit number
+- A CIDR range like `192.168.0.0/16` represents a continuous range of these numbers
+- For example, `192.168.0.0/16` includes all IPs from `192.168.0.0` to `192.168.255.255` *(65,536 addresses)*
+
+### The Magic of Linear Congruential Generators
+
+At the heart of PyLCG is something called a Linear Congruential Generator *(LCG)*. Think of it as a mathematical recipe that generates a sequence of numbers that appear random but are actually predictable if you know the starting point *(seed)*.
+
+Here's how it works:
+
+1. Start with a number *(called the seed)*
+2. Multiply it by a carefully chosen constant *(1597 in our case)*
+3. Add another carefully chosen constant *(51749)*
+4. Take the remainder when divided by 2^32
+5. That's your next number! Repeat the process to get more numbers
+
+In mathematical notation:
+```
+Next_Number = (1597 * Current_Number + 51749) mod 2^32
+```
+
+Why these specific numbers?
+
+- `1597` and `51749` were chosen because they create a sequence that:
+  - Visits every possible number before repeating *(maximum period)*
+  - Spreads numbers evenly across the range
+  - Can be calculated quickly on computers
+- `2^32` *(4,294,967,296)* is used because it:
+  - Matches the size of a 32-bit integer
+  - Is large enough to handle any IP range
+  - Makes calculations efficient on modern CPUs
+
+### Sharding: Dividing the Work
+
+Let's say you want to scan a /16 network *(65,536 IPs)* using 4 machines. Here's how PyLCG handles it:
+
+1. **Division**: First, it divides the total IPs evenly:
+   - 65,536 ÷ 4 = 16,384 IPs per shard
+   - Machine 1: IPs 0-16,383
+   - Machine 2: IPs 16,384-32,767
+   - Machine 3: IPs 32,768-49,151
+   - Machine 4: IPs 49,152-65,535
+
+2. **Randomization**: Within each shard, IPs are randomized using the LCG:
+   - Each IP index *(0 to 65,535)* is fed through the LCG
+   - The resulting numbers determine the scan order
+   - Because we use the same seed, this order is consistent across runs
+
+Example of how IPs might be ordered in Shard 1:
+```
+Original order: 0, 1, 2, 3, 4, 5...
+LCG values:    51749, 134238, 297019, 12983...
+Final order:   3, 5, 1, 4, 2, 0...  (sorted by LCG values)
+```
+
+### Memory-Efficient Processing
+
+To handle large IP ranges without consuming too much memory, PyLCG uses several techniques:
+
+1. **Chunked Processing**
+   Instead of loading all IPs at once, it processes them in chunks:
+   ```python
+   # Example with chunk_size = 1000
+   Chunk 1: Process IPs 0-999
+   Chunk 2: Process IPs 1000-1999
+   ...and so on
+   ```
+
+2. **Lazy Generation**
+   - IPs are generated only when needed using Python's async generators
+   - The system yields one IP at a time rather than creating huge lists
+   - This keeps memory usage constant regardless of IP range size
+
+3. **Direct Calculation**
+   - The LCG can jump directly to any position in its sequence
+   - No need to generate all previous numbers
+   - Enables efficient random access to any part of the sequence
+
+___
+
+## Real-World Applications
+
+### Network Security Testing
+
+Imagine you're testing the security of a large corporate network:
+- You have 5 scanning machines
+- You need to scan 1 million IPs
+- You want to avoid triggering IDS/IPS systems
+
+PyLCG helps by:
+1. Dividing the IPs evenly across your 5 machines
+2. Randomizing the scan order to avoid detection
+3. Allowing you to pause/resume scans from any point
+4. Using minimal memory on each machine
+
+### Cloud-Based Scanning
+
+In cloud environments, PyLCG is particularly useful:
+- Easily scale up/down the number of scanning instances
+- Each instance knows exactly which IPs to scan
+- Consistent results across multiple runs
+- Efficient resource usage keeps costs down
+
+___
+
+###### Mirrors for this repository: [acid.vegas](https://git.acid.vegas/pylcg) • [SuperNETs](https://git.supernets.org/acidvegas/pylcg) • [GitHub](https://github.com/acidvegas/pylcg) • [GitLab](https://gitlab.com/acidvegas/pylcg) • [Codeberg](https://codeberg.org/acidvegas/pylcg)
diff --git a/pylcg.py b/pylcg.py
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+#!/usr/bin/env python3
+# Python implementation of a Linear Congruential Generator for IP Sharding - Developed by acidvegas in Python (https://git.acid.vegas/pylcg)
+# pylcg.py
+
+import argparse
+import asyncio
+import ipaddress
+from math import ceil
+
+
+class LCG:
+    '''Linear Congruential Generator for deterministic random number generation'''
+
+    def __init__(self, seed: int, m: int = 2**32):
+        self.m       = m
+        self.a       = 1597
+        self.c       = 51749
+        self.seed    = seed
+        self.current = seed
+
+
+    def get_nth(self, n: int) -> int:
+        '''
+        Get the nth number in the sequence without generating previous numbers.
+
+        :param n: The index of the number to get
+        '''
+
+        # For large n, use the standard next() method to avoid modular arithmetic issues
+        if n > 1000:
+            self.current = self.seed
+            for _ in range(n):
+                self.next()
+            return self.current
+        
+        # For smaller n, use direct calculation
+        result = self.seed
+        for _ in range(n):
+            result = (self.a * result + self.c) % self.m
+        return result
+
+
+    def next(self) -> int:
+        '''Generate next random number'''
+
+        self.current = (self.a * self.current + self.c) % self.m
+
+        return self.current
+
+
+
+class IPRange:
+    '''Memory-efficient IP range iterator'''
+
+    def __init__(self, cidr: str):
+        network    = ipaddress.ip_network(cidr)
+        self.start = int(network.network_address)
+        self.end   = int(network.broadcast_address)
+        self.total = self.end - self.start + 1
+    
+    def get_ip_at_index(self, index: int) -> str:
+        '''
+        Get IP at specific index without generating previous IPs
+        
+        :param index: The index of the IP to get
+        '''
+
+        if not 0 <= index < self.total:
+            raise IndexError('IP index out of range')
+        
+        return str(ipaddress.ip_address(self.start + index))
+
+
+async def get_shard_ips(cidr: str, shard_num: int, total_shards: int, seed: int, chunk_size: int = 1000):
+    '''
+    Asynchronously generate IPs for the specified shard.
+    
+    :param cidr: The CIDR range to shard
+    :param shard_num: The number of the shard to generate
+    :param total_shards: The total number of shards
+    :param seed: The seed for the random number generator
+    :param chunk_size: The size of the chunks to process
+    '''
+
+    # Initialize the IP range and LCG
+    ip_range  = IPRange(cidr)
+    lcg       = LCG(seed)
+    total_ips = ip_range.total
+    
+    # Calculate which indices belong to this shard
+    shard_size = ceil(total_ips / total_shards)
+    start_idx  = shard_num * shard_size
+    end_idx    = min(start_idx + shard_size, total_ips)
+    
+    # Process in chunks to maintain memory efficiency
+    for chunk_start in range(start_idx, end_idx, chunk_size):
+        chunk_end     = min(chunk_start + chunk_size, end_idx)
+        chunk_indices = list(range(chunk_start, chunk_end))
+        
+        # Generate random values for this chunk
+        chunk_random_values = [(i, lcg.get_nth(i)) for i in chunk_indices]
+        chunk_random_values.sort(key=lambda x: x[1])
+        
+        # Yield IPs in randomized order
+        for idx, _ in chunk_random_values:
+            yield ip_range.get_ip_at_index(idx)
+            
+            # Allow other tasks to run (do we need this?)
+            await asyncio.sleep(0)
+
+
+async def main():
+    parser = argparse.ArgumentParser(description='Async IP address sharding tool')
+    parser.add_argument('cidr', help='Target IP range in CIDR format')
+    parser.add_argument('shard_num', type=int, help='Shard number (0-based)')
+    parser.add_argument('total_shards', type=int, help='Total number of shards')
+    parser.add_argument('--seed', type=int, default=12345, help='Random seed for LCG')
+    parser.add_argument('--chunk-size', type=int, default=1000, help='Processing chunk size')
+    
+    args = parser.parse_args()
+    
+    if args.shard_num >= args.total_shards:
+        raise ValueError('Shard number must be less than total shards')
+    
+    if args.shard_num < 0 or args.total_shards < 1:
+        raise ValueError('Invalid shard configuration')
+    
+    async for ip in get_shard_ips(args.cidr, args.shard_num, args.total_shards, args.seed, args.chunk_size):
+        print(ip)
+
+
+
+if __name__ == '__main__':
+    asyncio.run(main()) 
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diff --git a/unit_test.py b/unit_test.py
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+#!/usr/bin/env python3
+# Python implementation of a Linear Congruential Generator for IP Sharding - Developed by acidvegas in Python (https://git.acid.vegas/pylcg)
+# pylcg.py
+
+import unittest
+import asyncio
+import ipaddress
+import sys
+import time
+from pylcg import IPRange, get_shard_ips, LCG
+
+# ANSI color codes
+class Colors:
+    BLUE   = '\033[94m'
+    GREEN  = '\033[92m'
+    YELLOW = '\033[93m'
+    CYAN   = '\033[96m'
+    RED    = '\033[91m'
+    ENDC   = '\033[0m'
+
+def progress_bar(iteration: int, total: int, prefix: str = '', length: int = 50) -> None:
+    '''Simple progress bar using standard Python'''
+
+    percent = f"{100 * (iteration / float(total)):.1f}"
+    filled_length = int(length * iteration // total)
+    bar = '█' * filled_length + '-' * (length - filled_length)
+    sys.stdout.write(f'\r{Colors.CYAN}{prefix} |{bar}| {percent}%{Colors.ENDC} ')
+    if iteration == total:
+        sys.stdout.write('\n')
+    sys.stdout.flush()
+
+
+def print_header(message: str) -> None:
+    '''Print formatted header'''
+
+    print(f'\n{Colors.BLUE}{"="*80}')
+    print(f'TEST: {message}')
+    print(f'{"="*80}{Colors.ENDC}\n')
+
+
+def print_success(message: str) -> None:
+    '''Print success message'''
+
+    print(f'{Colors.GREEN}✓ {message}{Colors.ENDC}')
+
+
+def print_progress(message: str) -> None:
+    '''Print progress message'''
+
+    print(f"{Colors.YELLOW}⟳ {message}{Colors.ENDC}")
+
+
+class TestIPSharder(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        '''Set up test parameters'''
+        print_header('Setting up test environment')
+        cls.test_cidr = '192.0.0.0/16'  # 65,536 IPs
+        cls.test_seed = 12345
+        cls.total_shards = 4
+        cls.chunk_size = 1000
+        
+        # Calculate expected IPs
+        network = ipaddress.ip_network(cls.test_cidr)
+        cls.all_ips = {str(ip) for ip in network}
+        print_success(f"Initialized test environment with {len(cls.all_ips):,} IPs")
+    
+
+    def setUp(self):
+        '''Create event loop for each test'''
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
+    
+
+    def tearDown(self):
+        '''Clean up event loop'''
+        self.loop.close()
+    
+
+    async def collect_shard_ips(self, shard_num: int):
+        '''Helper to collect IPs from a shard'''
+
+        return {ip async for ip in get_shard_ips(self.test_cidr, shard_num, self.total_shards, self.test_seed, self.chunk_size)}
+    
+
+    def test_ip_range_initialization(self):
+        '''Test IPRange class initialization and calculations'''
+        print_header('Testing IPRange initialization')
+        ip_range = IPRange(self.test_cidr)
+        
+        self.assertEqual(ip_range.total, 65536)
+        print_success('IP range size correctly calculated')
+        
+        first_ip = ip_range.get_ip_at_index(0)
+        last_ip = ip_range.get_ip_at_index(ip_range.total - 1)
+        print_success(f'IP range spans from {first_ip} to {last_ip}')
+    
+
+    def test_shard_completeness(self):
+        '''Test that all IPs are covered exactly once across all shards'''
+        print_header('Testing shard completeness')
+        
+        async def check_completeness():
+            seen_ips = set()
+            shard_sizes = []
+            
+            for shard_num in range(self.total_shards):
+                progress_bar(shard_num, self.total_shards-1, prefix='Processing shards')
+                shard_ips = await self.collect_shard_ips(shard_num)
+                shard_sizes.append(len(shard_ips))
+                
+                # Check for duplicates and overlap
+                self.assertEqual(len(shard_ips), len(set(shard_ips)),
+                               f'Duplicates found in shard {shard_num}')
+                overlap = seen_ips & shard_ips
+                self.assertEqual(len(overlap), 0,
+                               f'Overlap found with previous shards: {overlap}')
+                
+                seen_ips.update(shard_ips)
+            
+            # Verify all IPs are covered
+            self.assertEqual(seen_ips, self.all_ips,
+                           'Not all IPs were covered by the shards')
+            print_success(f'All {len(self.all_ips):,} IPs were distributed across shards')
+            
+            # Print distribution information
+            for i, size in enumerate(shard_sizes):
+                print(f"{Colors.CYAN}Shard {i}: {size:,} IPs{Colors.ENDC}")
+        
+        self.loop.run_until_complete(check_completeness())
+    
+
+    def test_lcg_sequence(self):
+        '''Test LCG sequence generation and performance'''
+
+        print_header('Testing LCG sequence generation')
+        
+        lcg = LCG(seed=self.test_seed)
+        
+        # Test small sequence
+        small_n      = 100
+        start_time   = time.perf_counter()
+        small_result = lcg.get_nth(small_n)
+        small_time   = time.perf_counter() - start_time
+        print_success(f'Small sequence (n={small_n:,}) generated in {small_time:.6f}s')
+        
+        # Test large sequence
+        large_n      = 1_000_000
+        start_time   = time.perf_counter()
+        large_result = lcg.get_nth(large_n)
+        large_time   = time.perf_counter() - start_time
+        print_success(f'Large sequence (n={large_n:,}) generated in {large_time:.6f}s')
+        
+        # Verify deterministic behavior
+        lcg2 = LCG(seed=self.test_seed)
+        print_progress('Verifying sequence determinism...')
+        for i in range(large_n):
+            if i % (large_n // 100) == 0: # Update progress every 1%
+                progress_bar(i, large_n, prefix='Verifying sequence')
+            lcg2.next()
+        progress_bar(large_n, large_n, prefix='Verifying sequence')
+        
+        self.assertEqual(large_result, lcg2.current, 'LCG sequence is not deterministic')
+        print_success('LCG produces consistent results')
+
+
+
+if __name__ == '__main__':
+    print(f"\n{Colors.CYAN}{'='*80}")
+    print(f"Starting IP Sharder Tests - Testing with {65536:,} IPs (/16 network)")
+    print(f"{'='*80}{Colors.ENDC}\n")
+    unittest.main(verbosity=2) 
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