Search by email and nickname

Introduction

Greetings, dear readers! We continue the column of articles on the topic of OSINT called “SH”. In this article, we have prepared tools for you that will help you solve network intelligence tasks. Let's try to automate the search by e-mail and consider a multifunctional tool for searching for a nickname.

Disclaimer: All data provided in this article is taken from open sources, does not call for action and is only data for familiarization and study of the mechanisms of the technologies used.

Mosint email data search

What is this?

Mosint is an automated email OSINT research tool written in Go that allows you to research target email addresses quickly and efficiently. It integrates multiple services, allowing security researchers to quickly access a wealth of information.

Installation and use

There are several installation options:

Via go install:

go install -v github.com/alpkeskin/mosint/v3/cmd/mosint@latest

Via Docker:

git clone https://github.com/alpkeskin/mosint
docker build -t mosint
docker run mosint --help

Via go run:

git clone https://github.com/alpkeskin/mosint
cd v3/cmd
go run main.go

Now we create a configuration file in the home directory of the user from which we run the tool:

nano /home/kali/.mosint.yaml

Copy the text and paste it into the file being created:

#Config file for github.com/alpkeskin/mosint
#For default usage just rename this file to .mosint.yaml and put it in your home directory

services:
  breach_directory_api_key: SET_YOUR_API_KEY_HERE
  emailrep_api_key: SET_YOUR_API_KEY_HERE
  hunter_api_key: SET_YOUR_API_KEY_HERE
  intelx_api_key: SET_YOUR_API_KEY_HERE
  haveibeenpwned_api_key: SET_YOUR_API_KEY_HERE

settings:
  intelx_max_results: 20

As you can see, the tool uses the API of various services for searching for information leaks, so I will also provide a list of services and links to them for obtaining keys:

Now we try to scan the mail and see the following result:

Social-Analyzer unusual search by nickname

What is this?

Social Analyzer is a tool for analyzing and searching people's profiles on more than 1,000 social networks and websites. It includes various analysis and detection modules that allow you to select the desired features for your investigation.

Detection modules use different methods to score profiles from 0 to 100 (from “no information” to “probable”). This helps to minimize false positives.

Installation and use

The tool can be installed as a python module or by simply cloning the repository:

pip3 install social-analyzer

git clone https://github.com/qeeqbox/social-analyzer
cd social-analyzer
pip3 install -r requirements.txt

The tool has two modes of operation with and without a graphical interface. We will look at launching from two modes.

No GUI

First, let's get acquainted with all the parameters that are available to us at startup.

As we can see, the list is quite large. So we will analyze it using launch examples.

social-analyzer --username "test"

If you enter only the username without parameters, the tool will search for a long time across all available sites in its database, which will take about 5-10 minutes.

With parameter meatadata Information useful for investigations will be captured, such as user identification data, links to photographs, etc.

social-analyzer --username "test" --metadata

With parameter --top 100 will search one hundred popular sites.

social-analyzer --username "test" --metadata --top 100

With parameter --type a search will be performed on sites in a given category.

social-analyzer --username "test" --type "music"

Graphical interface

To launch, go to the tool directory and enter the following commands:

npm update
npm install
npm install loadash
npm start

After which it will show us the address where our graphical interface is located.

This is what the graphical interface looks like, let's try to enter the target nickname, select Fast Options and start searching by clicking analyze.

As we can see, this is where the data analysis tool's function comes into play. In the first screenshot, it shows information about the accounts found with their metadata. In the second, the tool builds a graph that combines the most likely accounts.

Overall, the tool can be extremely useful and easy to use, but again, you shouldn't rely solely on the account link probability algorithm, as it is far from perfect, and it's best to analyze and compare the information yourself.

Conclusion

The tools described in the article will make life much easier for network intelligence officers and OSINT specialists. And that's all we have, we will be glad to receive any additions and suggestions to the article!

P.S.
Useful OSINT tools in telegram:

Userbox

The Eye of God

Fun-stat-bot