Get data on IP addresses. Learn where they are located (lat/long, country, city, time zone), whether they are blacklisted or not (by abuseipdb, virustotal, ipvoid, etc.) and for what (and when they were blacklisted), which ports are open, and what services are running (via shodan), and what you get when you ping or issue a traceroute.
If you are curious about potential application of the package, we have a presentation on its use in cybersecurity analysis workflow.
You can use the package in two different ways. You can call it from the shell, or you can
use it as an external library. From the shell, you can run know_your_ip. It takes a csv
with a single column of IP addresses (sample input file: input.csv),
details about the API keys (in know_your_ip.cfg)
and which columns you would like from which service (in this example columns.txt),
and appends the requested results to the IP list (sample output file: output.csv).
This simple setup allows you to mix and match easily.
If you want to use it as an external library, the package also provides that. The function query_ip relies
on the same config files as know_your_ip and takes an IP address. We illustrate its use below. You can
also get data from specific services. For instance, if you only care about getting the MaxMind data,
use maxmind_geocode_ip. If you would like data from the abuseipdb, call the abuseipdb_api function, etc.
These functions still rely on the global config and columns files. For examples of how to use the package,
see example.py or the jupyter notebook example.ipynb.
Geocoding IPs: There is no simple way to discern the location of an IP. The locations are typically inferred from data on delay and topology along with information from private and public databases. For instance, one algorithm starts with a database of locations of various 'landmarks', calculates the maximum distance of the last router before IP from the landmarks using Internet speed, and builds a boundary within which the router must be present and then takes the centroid of it. The accuracy of these inferences is generally unknown, but can be fairly `poor.' For instance, most geolocation services place my IP more than 30 miles away from where I am. Try http://www.geoipinfo.com/.
The script provides hook to Maxmind City Lite DB. It expects a copy of the database to be in the folder in which the script is run. To download the database, go here. The function
maxmind_geocode_ipreturns city, country, lat/long etc.Timezone: In theory, there are 24 time zones. In practice, a few more. For instance, countries like India have half-hour offsets. Theoretical mappings can be easily created for lat/long data based on the 15 degrees longitude span. For practical mappings, one strategy is to map (nearest) city to time zone (recall the smallish lists that you scroll though on your computer's time/date program.) There are a variety of services for getting the timezone, including, but not limited to,
For its ease, we choose a Python hook to nodeJS lat/long to
timezone. To get the timezone, we first
need to geocode the IP (see above). The function tzwhere_timezone takes
lat/long and returns timezone.
Ping: Sends out a ICMP echo request and waits for the reply. Measures round-trip time (min, max, and mean), reporting errors and packet loss. If there is a timeout, the function produces nothing. If there is a reply, it returns:
packets_sent, packets_received, packets_lost, min_time, max_time, avg_time
Traceroute: Sends a UDP (or ICMP) packet. Builds the path for how the request is routed, noting routers and time.
Backgrounder:
- censys.io: Performs ZMap and ZGrab scans of IPv4 address space. To use censys.io, you must first register. Once you register and have the API key, put in here. The function takes an IP and returns asn, timezone, country etc. For a full list, see https://censys.io/ipv4/help.
- shodan.io: Scans devices connected to the Internet for services, open ports etc. You must register to use shodan.io. Querying costs money. Once you register and have the API key, put in here. The script implements two API calls: shodan/host/ip and shodan/scan. The function takes a list of IPs and returns
Blacklists and Backgrounders: The number of services that maintain blacklists is enormous. Here's a list of some of the services: TornevallNET, BlockList_de, Spamhaus, MyWOT, SpamRATS, Malc0de, SpyEye, GoogleSafeBrowsing, ProjectHoneypot, etc. Some of the services report results from other services as part of their results. In this script, we implement hooks to the following three:
- virustotal.com: A Google company that analyzes and tracks suspicious files, URLs, and IPs. You must register to use virustotal. Once you register and have the API key, put in here. The function implements retrieving IP address reports method.
- abuseipdb.com: Tracks reports on IPs. You must register to use the API. Once you register and have the API key, put in here. There is a limit of 5k pings per month. The function that we implement here is a mixture of API and scraping as the API doesn't return details of the reports filed.
- ipvoid.com: Tracks information on IPs. There is no API. We scrape information about IPs including status on various blacklist sites. ipvoid.com is now throttling requests so we have added apivoid.com, a paid version API access to ipvoid to know your ip.
| Service | Query Limits | More Info |
|---|---|---|
| Censys.io | 120/5 minutes | Censys Acct. |
| Virustotal | 4/minute | Virustotal API Doc. |
| AbuseIPDB | 2500/month | AbuseIPDB FAQ |
| IPVoid | - | |
| Shodan | - | |
| ----------- | ---------------- | ----------- |
The script depends on some system libraries. Currently traceroute uses
operating system command traceroute on Linux and tracert on
Windows.
Ping function is based on a pure python ping implementation using raw socket and you must have root (on Linux) or Admin (on Windows) privileges to run
# Install package and dependencies pip install know_your_ip # On Ubuntu Linux (if traceroute command not installed) sudo apt-get install traceroute
Note: If you use anaconda on Windows, it is best to install Shapely via:
conda install -c scitools shapely
To use the software, you need to take care of three things. You need to fill out the API keys in the config file, have a copy of MaxMind db if you want to use MaxMind, and pick out the columns you want in the columns.txt file:
- In the config file (default:
know_your_ip.cfg), there are settings grouped by function. - For Maxmind API, the script expects a copy of the database to be in
the folder specify by
dbpathin the config file. To download the database, go here - In the columns file (default:
columns.txt), there are the data columns to be output by the script. We may have more than one columns file but only one will be use by setting thecolumnsvariable inoutputsection. - One more thing re. MaxMind--- you can comment out line 118 and 119 in know_your_ip.py if you don't have a userid or API Key as Maxmind is also available for free. (see issue)
Most of functions make calls to different public REST APIs and hence require an API key and/or username. You can register to get the API keys at the following URLs:
See this example know_your_ip.cfg
We can also select the data columns which will be outputted to the CSV file in the text file. To take out that column from the output file, add
#at the start of line in the text filecolumns.txt.
usage: know_your_ip [-h] [-f FILE] [-c CONFIG] [-o OUTPUT] [-n MAX_CONN]
[--from FROM_ROW] [--to TO] [-v] [--no-header]
[ip [ip ...]]
Know Your IP
positional arguments:
ip IP Address(es)
optional arguments:
-h, --help show this help message and exit
-f FILE, --file FILE List of IP addresses file
-c CONFIG, --config CONFIG
Configuration file
-o OUTPUT, --output OUTPUT
Output CSV file name
-n MAX_CONN, --max-conn MAX_CONN
Max concurrent connections
--from FROM_ROW From row number
--to TO To row number
-v, --verbose Verbose mode
--no-header Output without header at the first row
know_your_ip -file input.csv
Please also look at example.py or the jupyter notebook example.ipynb.
import pandas as pd
from know_your_ip import load_config, query_ip
df = pd.read_csv('know_your_ip/examples/input.csv', header=None)
args = load_config('know_your_ip/know_your_ip.cfg')
odf = df[0].apply(lambda c: pd.Series(query_ip(args, c)))
odf.to_csv('output.csv', index=False)
For more information, please see project documentation.
Suriyan Laohaprapanon and Gaurav Sood
The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.
The package is released under the MIT License.