Pinging the internet

Inspired by Tom7's Harder Drive project, this repo provides the utilities to ping every IP address in the IPv4 address space.

Table of Contents

• TODO
• Create IP List
  • Exponential Range
  • Ordered Range
• Pinging IPs
• Docker
  • Creating image
  • Running image
• Image Output
  • Active Spaces
  • Dead Spaces
• Benchmarks
  • Active Spaces Bench
  • Dead Spaces Bench

TODO

• Modify Docker implementation.
• Live Benchmarks.
• Network and Memory performance accuracy.

Create IP List

There are 2^32 possible IP addresses, or around 4.2 billion addresses. The output list will be stored in ip_list.txt.

Exponential Range

To skip addresses and create a list within a range of range^4, use the list.py script:

sudo python3 list.py <range>

Provide a 0-256 range value. NOTE: This script requires elevated privilages to write to the output file

Example Usage:

sudo python3 list.py 1

Output:

0.0.0.0

Ordered Range

To create an IP list between a defined IP range, use the range.py script:

sudo python3 range.py <start_range> <end_range>

Provide a start and end range between 0.0.0.0 to 255.255.255.255.

Example Usage:

sudo python3 range.py 0.0.0.0 0.0.0.5

Output:

0.0.0.0
0.0.0.1
0.0.0.2
0.0.0.3
0.0.0.4
0.0.0.5

Pinging IPs

All responses will output to icmp_responses.txt. See the sample output below. NOTE: Sending ICMP echo requires elevated permissions, use sudo

1 is alive, 0 is dead.

0.0.0.0,0
0.0.0.1,0
0.0.1.0,0
0.0.1.1,0
0.1.0.0,0
0.1.0.1,0
0.1.1.0,0
0.1.1.1,0
1.0.0.0,0
1.0.0.1,1
1.0.1.0,0
1.0.1.1,0
1.1.0.0,0
1.1.0.1,0
1.1.1.0,1
1.1.1.1,1

Run with the following command:

sudo python3 ping.py

Docker

To run the process in docker, follow the steps below.

Creating image

To create the image, run the build process by running:

docker build -t pingy .

Running image

To run the image after building it, run the following command:

docker run -d pingy

Image Output

To view the results as a bitmap image with black pixels representing dead hosts, and white pixels representing alive hosts, use the map.py script:

NOTE: If you run this with elevated privilages, you will get permission issues viewing the image.

python3 bitmaps/map.py

View the sample outputs below:

Active Spaces

Active spaces are ranges that are most populated, returning in active responses.

1. 104.16.181.0 - 104.16.220.15 (10,000 Addresses)

2. 104.16.181.0 - 104.18.59.159 (100,000 Addresses)

Dead Spaces

Dead spaces are ranges that aren't commonly populated, returning minimal responses.

Active Space Benchmarks

Benchmarks from Active Spaces

Addresses	Time (seconds)	Network (bytes)	Network (MB)	Memory (bytes)	Memory (MB)
10,000	73.40	1025024	1.025	152223744	152.22
100,000	632.77	10585088	10.59	100925440	100.93

Dead Space Benchmarks

Benchmarks from Dead Spaces

Addresses	Time (seconds)	Network (bytes)	Network (MB)	Memory (bytes)	Memory (MB)
6,561	533.64	1108992	1.11	39305216	39.31