Typodetect - Detect The Active Mutations Of Domains

Typodetect – Detect The Active Mutations Of Domains

This tool gives blue teams, SOC’s, researchers and companies the ability to detect the active mutations of their domains, thus preventing the use of these domains in fraudulent activities, such as phishing and smishing.

For this, Typodetect allows the use of the latest available version of the TLDs (Top Level Domains) published on the IANA website, the validation of decentralized domains in Blockchain DNS and the malware reports in DoH services (DNS over HTTPS) .

For the ease of the user, Typodetect delivers the report in JSON format by default, or in TXT format, depending on how the user selects and shows on the screen a summary of the mutations generated, the active domains and the reports detected with Malware or decentralized domains.


Clone this repository with:

git clone https://github.com/Telefonica/typodetect

Run setup for installation:

python3 pip install -r requirements.txt

Running TypoDetect

Inside the TypoDetect directory:

python3 typodetect.py -h
usage: typodetect.py [-h] [-u UPDATE] [-t N_THREADS] [-d DOH_SERVER] [-o OUTPUT] domain

positional arguments:
domain specify domain to process

optional arguments:
-h, --help show this help message and exit
-u UPDATE, --update UPDATE
(Y/N) for update TLD's database (default:N)
-t N_THREADS, --threads N_THREADS
Number of threads for processing (default:5)
Section DoH for use: [1] ElevenPaths (default) [2] Cloudfare
-o OUTPUT, --output OUTPUT
JSON or TXT, options of filetype (default:JSON)

For a simple analysis:

python3 typodetect.py <domain>

For update IANA database and analysis:

python3 typodetect.py -u y <domain>

For more threads analysis:

python3 typodetect.py -t <number of threads> <domain>

For a different DoH (currently only has ElevenPaths o CloudFare)

python3 typodetect.py -d 2 <domain>

For create TXT report

python3 typodetect.py -o TXT <domain>


Inside the reports directory, the report file is saved, by default in JSON, with the name of the analyzed domain and the date, for example:


The JSON report has the following structure for each active mutation detected:

{ id: 
"report_DoH" : <string>
"domain": <string>
"A": [ip1, ip2, ...]
"MX": [mx1, mx2, ...]

The fields contain the following information:

id: Integer id of mutation
"report_DoH": "" - Domain of Descentralised DNS
"Malware" - Domain reported as dangerous for DoH
"Good" - Domain reported as good for DoH
"domain": Mutation detected as active.
"A": IP's address of A type in DNS of the mutation.
"MX": IP's or CNAME of MX type in DNS of the mutation.

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