Latest revision as of 19:57, 11 May 2025

Old Notes

Approaches

There are a couple of different ways to do wireless attacks with Python.

Joe Pesci Approach: Besside-ng

This way is painful: besside-ng. besside-ng is like the Joe Pesci of the wireless attack world. Joe Pesci speaks softly and carries a big stick. You give Joe Pesci a MAC number, and just sit back while Joe Pesci gets things done.

Scapy Approach: Mellow Out

The Python way: make things a lot easier for yourself, and let the Scapy Python library do all the parsing of information. Run airodump or similar in the background to make the wireless card channel hop. Run Scapy to parse out all the information that's being collected. (Details?) You still have to scan to find nearby devices/routers, but it makes information management a whole lot easier.

See Wireless/Python/Scapy

New Notes

Wireless-radar

Interesting Python package: https://pypi.python.org/pypi/wireless-radar/0.2

wireless-radar comes with a few tools:

wprox a scanner for detecting/fingerprinting active 802.11 devices
mrssi a simple RSSI sensor locking onto a MAC for physically locating the device
wscan a direction-finder using a directional antenna mounted on a usb rocket launcher
bprox a Bluetooth device discoverer
rfdiff to diff the outputs of wprox scans

Github Repositories

Nosecleaner Github Repo

Multiple useful scripts in this repository, for each step of the wireless toolchain. Should be revisited with more thought paid to the toolchain objectives and different use cases, however.

https://github.com/charlesreid1/nosecleaner

Wifi Data Github Repo

Random assortment of scripts. Figure out what's what. Make an attic.

http://github.com/charlesreid1/wifi-data

New Wifi Data Github Repo

New Github repository for the UGR project. Initially, it will mainly be a way of sharing files with them. Read-only.

Projects

UGR Project

Main page: UGR Project

The scope of the UGR project is to run Linux and Python on Raspberry Pi computers, and capture data from them.

Right now, the plan is to capture wireless data on a C2 server. Not sure what else to do.

If we were to use other data as a model [1]: pollution, dust, light, sound, temperature, humidity

Raspberry Pi could measure pollution, dust, light, sound, temperature, humidity, and cameras and wifi to analyze traffic

Weather timelapse: superimposed weather sensor data with timelapse movie: http://datacanvas.org/project/datacanvas-weather-timelapse/

Pi Data Acquisition

RasbperryPi/Data Acquisition

Script/scripts for doing data acquisition of time series from Raspberry Pi.

Similar quantities to what a smartphone time series data set might contain - CPU usage, memory usage, programs, network names, etc.

Flags

@@ Line 1: / Line 1: @@
-=Approaches=
+=Old Notes=
+==Approaches==
 There are a couple of different ways to do wireless attacks with Python.
-==The One Man Band Approach==
+===Joe Pesci Approach: Besside-ng===
+This way is painful: besside-ng. besside-ng is like the Joe Pesci of the wireless attack world. Joe Pesci speaks softly and carries a big stick. You give Joe Pesci a MAC number, and just sit back while Joe Pesci gets things done.
+===Scapy Approach: Mellow Out===
+The Python way: make things a lot easier for yourself, and let the [[Scapy]] Python library do all the parsing of information. Run airodump or similar in the background to make the wireless card channel hop. Run Scapy to parse out all the information that's being collected. (Details?) You still have to scan to find nearby devices/routers, but it makes information management a whole lot easier.
+See [[Wireless/Python/Scapy]]
+=New Notes=
+==Wireless-radar==
+Interesting Python package: https://pypi.python.org/pypi/wireless-radar/0.2
+wireless-radar comes with a few tools:
+* wprox a scanner for detecting/fingerprinting active 802.11 devices
+* mrssi a simple RSSI sensor locking onto a MAC for physically locating the device
+* wscan a direction-finder using a directional antenna mounted on a usb rocket launcher
+* bprox a Bluetooth device discoverer
+* rfdiff to diff the outputs of wprox scans
+=Github Repositories=
+==Nosecleaner Github Repo==
+Multiple useful scripts in this repository, for each step of the wireless toolchain. Should be revisited with more thought paid to the toolchain objectives and different use cases, however.
+https://github.com/charlesreid1/nosecleaner
+==Wifi Data Github Repo==
+Random assortment of scripts. Figure out what's what. Make an attic.
+http://github.com/charlesreid1/wifi-data
-The first way is sort of painful, or can overload your system: trying to find every wireless network, parsing out clients and access points, listening, identifying and counting packets and unique devices, and managing all of this information. Lots of moving parts. Very painful. Complicated. But you have fine-grained control over every detail.
+==New Wifi Data Github Repo==
-You end up feeling like a one man band.
+New Github repository for the UGR project. Initially, it will mainly be a way of sharing files with them. Read-only.
-==Scapy Approach: Mellow Out==
+=Projects=
-The second way: make things a lot easier for yourself, and let the [[Scapy]] Python library do all the parsing of information. Run airodump or similar in the background to make the wireless card channel hop. Run Scapy to parse out all the information that's being collected. (Details?) You still have to scan to find nearby devices/routers, but it makes information management a whole lot easier.
+==UGR Project==
-==Joe Pesci Approach: Besside-ng==
+Main page: [[UGR Project]]
-The third way is least painful: besside-ng. besside-ng is like the Joe Pesci of the wireless attack world. Joe Pesci gets things done with a baseball bat. You give Joe Pesci a MAC number, and just sit back while Joe Pesci gets things done.
+The scope of the UGR project is to run Linux and Python on Raspberry Pi computers, and capture data from them.
+Right now, the plan is to capture wireless data on a C2 server. Not sure what else to do.
-For scripts, see the Nosecleaner project on Github: https://github.com/charlesreid1/nosecleaner
+If we were to use other data as a model [http://datacanvas.org/sense-your-city/]: pollution, dust, light, sound, temperature, humidity
+Raspberry Pi could measure pollution, dust, light, sound, temperature, humidity, and cameras and wifi to analyze traffic
+Weather timelapse: superimposed weather sensor data with timelapse movie: http://datacanvas.org/project/datacanvas-weather-timelapse/
+==Pi Data Acquisition==
+[[RasbperryPi/Data Acquisition]]
+Script/scripts for doing data acquisition of time series from Raspberry Pi.
+Similar quantities to what a smartphone time series data set might contain - CPU usage, memory usage, programs, network names, etc.
+=Flags=
+{{PythonFlag}}
+{{WirelessFlag}}
 {{AircrackFlag}}
-[[Category:Wireless]]
-[[Category:Python]]

Wireless/Python: Difference between revisions

From charlesreid1