sniffdemo

sniffdemo is a fundamentally crude iteration of what our underlying sniffer tool will be able to achieve once all the required modules have been integrated.

Repos considered:

Modifiable repos:

These repos contain algorithms that may be considered when developing our sniffer tool

LTESniffer - The signal decoding algorithms used in this repo are pretty efficient, in both the uplink/downlink. The tool itself is a passive one, which implies that the IMSI is only captured when there is initial information handover between a UE (user device) and eNB (cell tower) - we want to instead be using active attacks which brings us to
SigOverinjector - This is essentially the next-gen FBS (fake base station) attack, except it does not disrupt

Foundational repos:

These repos will be required in its full capacity - i.e., there will be very little modification to the code here - this might change with time

Current concerns/challenges

All sub-modules and building blocks to be used for a real-world iteration of sniffdemo have been included in /external, but none have as yet been implemented within main.cpp.
The sniffer already knows device configurations, and in fact, doesn't even require SDR connection to 'sniff' the signals. It is essentially faking the process of sniffing.
All executions and function defintions are made within the main.cpp file, which is sub-optimal.

Dependencies

sudo apt update
sudo apt-get install autoconf automake build-essential ccache cmake cpufrequtils doxygen ethtool \
g++ git inetutils-tools libboost-all-dev libncurses5 libncurses5-dev libusb-1.0-0 libusb-1.0-0-dev \
libusb-dev python3-dev python3-mako python3-numpy python3-requests python3-scipy python3-setuptools \
python3-ruamel.yaml

building and executing

chmod +x configure.sh build.sh run.sh
sudo ./configure.sh
sudo ./build.sh
sudo ./run.sh

Next steps

Immediate future (~1 month)

Get all sub-modules integrated within the main program, and be reliably identifying devices and storing information

Near future (~3 months)

Integrate camera identification algorithm based on the C++ library dlib
Start training a NeuralNet that will be matching faces with IMSI values from the stored database
Develop a web application using Flutter that will demonstrate how all of this will work on the user side

apex-jpg / activsniff