Wireless/Python - Revision history

Unknown user: /* The One Man Band Approach */

2025-05-11T19:57:04Z

The One Man Band Approach

← Older revision		Revision as of 19:57, 11 May 2025
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	There are a couple of different ways to do wireless attacks with Python.		There are a couple of different ways to do wireless attacks with Python.

	~~===<s>The One Man Band Approach</s>===~~

	<s>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.

	~~You end up feeling like a one man band.</s>~~

	~~For scripts, see the Nosecleaner project on Github: https://github.com/charlesreid1/nosecleaner~~

	===Joe Pesci Approach: Besside-ng===		===Joe Pesci Approach: Besside-ng===

Admin: /* Pi Data Acquisition */

2017-02-26T06:11:36Z

Pi Data Acquisition

← Older revision		Revision as of 06:11, 26 February 2017
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	==Pi Data Acquisition==		==Pi Data Acquisition==

	Script/scripts for ~~acquiring~~ time series ~~data~~ from Raspberry Pi.		[[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.		Similar quantities to what a smartphone time series data set might contain - CPU usage, memory usage, programs, network names, etc.

Admin: /* Projects */

2017-02-26T06:10:32Z

Projects

← Older revision		Revision as of 06:10, 26 February 2017
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	Weather timelapse: superimposed weather sensor data with timelapse movie: http://datacanvas.org/project/datacanvas-weather-timelapse/		Weather timelapse: superimposed weather sensor data with timelapse movie: http://datacanvas.org/project/datacanvas-weather-timelapse/

			==Pi Data Acquisition==

			Script/scripts for acquiring time series data from Raspberry Pi.

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

	=Flags=		=Flags=

Admin: /* Kali Wireless Redux */

2017-02-26T05:10:16Z

Kali Wireless Redux

← Older revision		Revision as of 05:10, 26 February 2017
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	=New Notes=		=New Notes=

	~~==Kali Wireless Redux==~~

	~~Getting the Kali wireless workflow up and operational.~~

	==Wireless-radar==		==Wireless-radar==

Admin: /* Kali Wireless Redux */

2016-08-30T11:18:05Z

Kali Wireless Redux

← Older revision		Revision as of 11:18, 30 August 2016
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	Getting the Kali wireless workflow up and operational.		Getting the Kali wireless workflow up and operational.

			==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=		=Github Repositories=

Admin: /* Kali Wireless Redux */

2016-08-18T03:03:19Z

Kali Wireless Redux

← Older revision		Revision as of 03:03, 18 August 2016
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	==Kali Wireless Redux==		==Kali Wireless Redux==

	Getting the workflow ~~back~~ up and operational.		Getting the Kali wireless workflow up and operational.

	=Github Repositories=		=Github Repositories=

Admin: /* Github Repositories */

2016-08-18T03:03:05Z

Github Repositories

← Older revision		Revision as of 03:03, 18 August 2016
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	See [[Wireless/Python/Scapy]]		See [[Wireless/Python/Scapy]]

			=New Notes=

			==Kali Wireless Redux==

			Getting the workflow back up and operational.

	=Github Repositories=		=Github Repositories=

Admin: /* Approaches */

2016-08-18T03:02:33Z

Approaches

← Older revision		Revision as of 03:02, 18 August 2016
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	=Approaches=		=Old Notes=

			==Approaches==

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

	==<s>The One Man Band Approach</s>==		===<s>The One Man Band Approach</s>===

	<s>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.		<s>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.
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	For scripts, see the Nosecleaner project on Github: https://github.com/charlesreid1/nosecleaner		For scripts, see the Nosecleaner project on Github: https://github.com/charlesreid1/nosecleaner

	==Joe Pesci Approach: Besside-ng==		===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.		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==		===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.		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.

Admin at 02:54, 18 August 2016

2016-08-18T02:54:11Z

← Older revision		Revision as of 02:54, 18 August 2016
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	See [[Wireless/Python/Scapy]]		See [[Wireless/Python/Scapy]]

			=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 [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/

			=Flags=

			{{PythonFlag}}

			{{WirelessFlag}}

	{{AircrackFlag}}		{{AircrackFlag}}

	~~[[Category:Wireless]]~~
	~~[[Category:Python]]~~

Admin: /* Approaches */

2016-01-24T05:58:37Z

Approaches

← Older revision		Revision as of 05:58, 24 January 2016
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	There are a couple of different ways to do wireless attacks with Python.		There are a couple of different ways to do wireless attacks with Python.

	==The One Man Band Approach==		==<s>The One Man Band Approach</s>==

	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.		<s>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.

	You end up feeling like a one man band.		You end up feeling like a one man band.</s>

	~~==Scapy Approach~~: ~~Mellow Out==~~		For scripts, see the Nosecleaner project on Github: https://github.com/charlesreid1/nosecleaner

	~~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.		==Joe Pesci Approach: Besside-ng==

	~~See [[Wireless/Python/Scapy]]~~		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.

	==~~Joe Pesci~~ Approach: ~~Besside-ng~~==		==Scapy Approach: Mellow Out==

	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 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]]

	~~For scripts, see the Nosecleaner project on Github: https://github.com/charlesreid1/nosecleaner~~

	{{AircrackFlag}}		{{AircrackFlag}}