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Nzyme: A WiFi defense system for detecting ‘bandit’ devices

Platform aims to shore up lax wireless security and eradicate WiFi spoofing attacks

Nzyme: A WiFi defense system for detecting bandit WiFi devices and hacking

A new platform designed to detect WiFi hijacking devices has been released to the open source community.

The system, dubbed ‘Nzyme’, has been published under the v1.0 moniker ‘Kyle Canyon’ on GitHub.

Nzyme is the result of three years’ worth of “weekend hacking” by developer Lennart Koopmann and is described as a “free and open WiFi defense system that detects and physically locates threats using an easy to build and deploy sensor system”.

WiFi attacks

There are a variety of ways to defend today’s wireless networks, including enabling WPA2 encryption, changing default password setups, and enabling firewalls. Wireless intrusion detection systems (IDS) can also be used to monitor external entry attempts.

However, the developer says that current IDS solutions “fall short and are easily spoofed”, and could be broken by out-of-the-box, commercially available hacking tools.

“Existing tools are focusing a lot on signature-based detection,” Koopmann told The Daily Swig. “They look for anomalies and outliers, but I found that WiFi communication is almost effortless to spoof, and anomaly detection has very high false-positive rates, especially in wireless environments.”

To combat these devices, including but not limited to offerings such as the WiFi Pineapple and Pwnagotchi, Nzyme – which can be used as a portable tracker device – has built-in definitions for “out-of-the-box bandit” devices that will detect them the moment they are “powered on and in range”, the developer says.


Nzyme is an open source tool that detects bandit WiFi devices and attacksNzyme monitors the entire WiFi spectrum in the hunt for so-called ‘bandit’ devices

Under the HUD

The platform’s overview screen includes system status, active alerts, timestamps for intrusion or anomalies detected, probe logs, channel traffic monitoring, and fingerprint records.

“Nzyme and its ‘bandits’ concept can detect many of these platforms the moment they power on, often even before an attack is executed, and then even track specific devices,” Koopmann explained.

“This ability massively increases your security posture in a notoriously under-secured and easy-to-attack environment like WiFi.”


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Nzyme can be tailored to use built-in definitions or custom alerts, although bandit definitions considered to be too wide-ranging may result in false positives.

“A Nzyme tracker device can be used to physically locate the source of specific WiFi frames and play a big role in actively defending your perimeter,” the developer added, noting this concept is currently being explored through both LoRa and 802.11 WiFi standards.

Active development

When queried over the potential use of clone SSID denial-of-service (DoS) techniques in the future – or keep the software’s capabilities to alert-only – Koopmann said there are no plans, at present, to introduce “fire back” options, but the callback system for alerts will soon be upgraded to trigger local scripts which will extend Nzyme further.

As a new project now in active development, naturally there are bugs and hurdles to overcome.

A future improvement on the Nzyme roadmap is multi-node support and potentially the introduction of a node type that could allow sensors to be deployed to extend coverage, or perhaps go so far as to triangulate bandits.

“Currently, I am looking for community feedback after this initial release,” Koopmann commented. “Nzyme is my weekend project, and all feedback and contributions are always welcome.”


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Sours: https://portswigger.net/daily-swig/nzyme-a-wifi-defense-system-for-detecting-bandit-devices

nzyme: WiFi Defense System

What is nzyme?

The nzyme project uses WiFi adapters in monitor mode to scan the frequencies for suspicious behavior, specifically rogue access points and known WiFi attack platforms. Each recorded wireless frame is parsed and optionally sent to a Graylog log management system for long-term storage that allows you to perform forensics and incident response. Ever wondered what to do if you catch a malicious wireless actor? With nzyme, you will be able to reconstruct what happened, who was targeted, and who was successfully compromised.

Several types of alerts are automatically raised. The employed techniques range from a signature-based analysis of expected network infrastructure, threat landscape assessment with fingerprinting to setting traps with deception capabilities.

What is nzyme not?

nzyme is not designed to be physically moving around in any way. It is supposed to stay stationary and constantly observe the WiFi radio frequency spectrum. If you are looking for a WiFi recon or wardriving tool, you should check out Kismet.

(It obviously won’t break from moving around but the interface and some of the functionality won’t make much sense anymore.)

Install & Use

Sours: https://securityonline.info/nzyme-wifi-defense-system/
  1. Needles for drugs
  2. Yoga with adriene slow flow
  3. Nike signal shirt

nzyme - WiFi Defense System

Build and TestCodecovLicense

Screenshot

Introduction

What is nzyme?

The nzyme project uses WiFi adapters in monitor mode to scan the frequencies for suspicious behavior, specifically rogue access points and known WiFi attack platforms. Each recorded wireless frame is parsed and optionally sent to a Graylog log management system for long-term storage that allows you to perform forensics and incident response. Ever wondered what to do if you catch a malicious wireless actor? With nzyme, you will be able to reconstruct what happened, who was targeted, and who was successfully compromised.

Several types of alerts are automatically raised. The employed techniques range from signature based analysis of expected network infrastructure, threat landscape assessment with fingerprinting to setting traps with deception capabilities.

What is nzyme not?

nzyme is not designed to be physically moving around in any way. It is supposed to stay stationary and constantly observe the WiFi radio frequency spectrum. If you are looking for a WiFi recon or wardriving tool, you should check out Kismet.

(It obviously won't break from moving around but the interface and some of the functionality won't make much sense anymore.)

Getting Started

Please visit the getting started page to get started.

Contributing

There are many ways to contribute and all community interaction is absolutely welcome:

  • Open an issue for any kind of bug you think you have found.
  • Open an issue for anything that was confusing to you. Bad, missing or confusing documentation is considered a bug.
  • Open a Pull Request for a new feature or a bugfix. It is a good idea to get in contact first to make sure that it fits the roadmap and has a chance to be merged.
  • Write documentation.
  • Write a blog post.
  • Help a user in the issue tracker or the IRC channel (#nzyme on FreeNode.)
  • Get in contact and say how you use it or what would be a cool addition.
  • Tell the world.

Please be aware of the Code of Conduct that will be enforced across all channels and platforms.

Legal notice

Make sure to comply with local laws, especially with regards to wiretapping, when running nzyme. Note that nzyme is never decrypting any data but only reading unencrypted data on license-free frequencies.

Sours: https://github.com/lennartkoopmann/nzyme
Nzyme - Paul Asadoorian \u0026 Larry Pesce - PSW 711

Nzyme Project – An Inclusive WiFi Defense System To Detect Bandits

A researcher has devised a new strategy to protect wireless networks. Dubbed nzyme, the tool serves as a dedicated WiFi defence system to thwart network hacking attempts. The tool open source tool is available on GitHub.

Nzyme WiFi Defense Project

The security researcher and developer Lennart Koopmann has recently released an open-source WiFi defence system named ‘Nzyme’. The tool serves as an advanced wireless network protection mechanism that detects potential hacking attempts.

Sharing the details, Koopmann revealed that the tool makes use of WiFi adapters to monitor the wireless spectrum. It scans the network and generates alerts as soon as it detects any suspicious behavior. This includes detecting any network hacking attempts via established or novel tools.

Describing the purpose of Nzyme, Koopmann stated in a post,

Existing WiFi IDS systems fall short and can be easily spoofed, even by not very sophisticated attackers with commoditized attack platforms. Wireless networks open a huge attack vector and exploiting it is easy compared to alternative vectors.

Hence, Nzymes serves as a proactive defense mechanism to identify and locate the threats such as WiFi Pineapple or Pwnagotchi.

The nzyme project uses WiFi adapters in monitor mode to scan the frequencies for suspicious behavior, specifically rogue access points and known WiFi attack platforms. Each recorded wireless frame is parsed and optionally sent to a Graylog log management system for long-term storage that allows you to perform forensics and incident response.

Nzyme doesn’t need physical relocation. Rather it requires stationary installation for monitoring the WiFi spectrum.

The researcher has shared screenshots of the tool in the post on Nzyme’s website. Whereas the following video is a demonstration of Nzyme detecting Pwnagotchi.

What Next?

The tool is currently in its development phase, having the first version released as Nzyme v1.0 “Kyle Canyon”.

Sours: https://latesthackingnews.com/2021/04/05/nzyme-project-an-inclusive-wifi-defense-system-to-detect-bandits

Wifi nzyme

The Personal Blog of Lennart Koopmann _

by Lennart Koopmann

Today I am releasing my latest open source hobby project: nzyme. It's a Java-based program that puts wireless network adapters into monitor mode, sniffs management frames from all configured 2.4Ghz or 5Ghz channels and writes them into a Graylog instance for monitoring and analysis.

Nzyme

About

In my previous post, Common WiFi Attacks And How To Detect Them, I laid out the many ways an attacker can attack a wireless network.

With this post, I am introducing nzyme, an open source tool used to detect these attacks or to perform incident response after an attack has happened.

My talk about WiFi security and nzyme at Derbycon 2017 was recorded and can be seen here:

Important: Note that Graylog v2.4 (to be released in about two weeks) has significant visualization improvements that will make the analysis shown in some of the use-cases even easier.

Dashboard

Why?

There is a problem with existing approaches. Tools like Kismet are great to detect an attack, but they don’t store all frames that you would need for a proper response to an alert.

OK, there was a targeted rogue AP attack, but I have no way to find out who connected to it, for how long it was there or where it was located physically.

Wireshark/tcpdump can collect the frames but are both falling short on long-term collection and analysis. It's almost impossible to detect a rogue AP by MAC timestamp or signal strength anomalies. Imagine how long it would take to simply load a PCAP file worth a few days of data.

This is where nzyme comes in: By writing the data into Graylog, you can analyze and visualize months worth of data in a few milliseconds - even on modest hardware.

How does it work?

Nzyme reads 802.11 WiFi frames directly from the air using any WiFi adapter that supports monitor mode. It then parses the frames and sends them over the network to a Graylog (free and open source log management) setup.

For example, a recorded frame would look like this in Graylog:

Screen-Shot-2017-09-22-at-5.39.20-PM

Noisy environments can easily create hundreds of messages per second, and with all the data collected in Graylog, you can analyze the WiFi traffic in many ways. A set of interesting use-cases follows below.

The more detailed description and installation instructions can be found in the README.

Use-cases

Threat and intrusion detection

This is what Kismet already does well, but nzyme can help with threat hunting approaches.

Deauth flooding

Deauthentication flooding can be an attempt to simply jam and disrupt communications or a stage of an attack, where the attacker tries to get the victim off a legitimate access point so the targeted device may connect to a rogue access point operated by the attacker.

We can detect this by having an eye on the number of deauthentication (deauth) frames in the air:

Graylog query:

Detecting deauthentication floods

As you can see, there is a usual amount of deauth frames in the air around 10 pm. Set up a threshold alert in Graylog to trigger actions anytime there are too many frames.

BSSID whitelisting

Every access point in a WiFi network has a MAC address that is called BSSID ("Basic Service Set Identifier"). A less sophisticated attacker would not bother to spoof the BSSID of an existing access point, and suddenly you have an unknown MAC address flying around. By keeping a list of the BSSIDs of your access points and searching for any others that are pretending to serve your network (SSID), you can find those less sophisticated rogue access points.

Create a CSV file on your Graylog server that looks like this:

Load this CSV file into a Graylog Lookup Table and compare any incoming or frame for your SSID against this list. Here is an example of how to do this with a Graylog Processing Pipeline:

What happens here is that the function compares the field of the Graylog message against the lookup table. The lookup table is configured to return if it does not match a value from the CSV file or the name of the access point as if it does match.

The result (either or the access point name) is written into a new field called .

Now we can search for all or frames for our SSID that were sent from a device that is not ours:

Graylog Query:

As mentioned before, it is easy to spoof a BSSID, so this will not help against more sophisticated attackers. However, given how easy this detection is, it is still a good idea to configure an alert for this in Graylog.

Non-synchronized MAC timestamps

It is important that every access point that spawns the same network has a highly synchronized internal clock. For that reason, the access points are constantly exchanging timestamps for synchronization in their beacon frames. The unit here is microseconds, and the goal is to stay synchronized within a delta of 25µs.

Some rogue access points will not attempt to synchronize the timestamps properly, and you can detect that slip.

You can analyze the timestamp across all access points over time in Graylog with the Generate chart field analyzer:

Graylog query:

Analyzing MAC timestamps

The screenshot you are looking at has a perfect MAC timestamp pattern, but an unsynchronized (rogue) access point would distort it. Note that I have not found a rogue AP yet that does not synchronize the timestamps and also that a short slip or lag might be really hard to detect. Oh, and the reset that happens from time to time is food for false positive alerts.

Beacon counts

Access points of the same type and in the same BSS (your network) will usually be sharing the same beacon frequency. A rogue access point may not synchronize with that frequency and send at a much higher frequency.

Graylog query:

Beacon intervals

(Note that I'm using a workaround here by counting the total frames in a custom chart. Graylog v2.4. will make it possible to chart this directly out of a Quick Values analysis with one click.)

Signal strength anomalies

This is my favorite one because it is so hard to hide without having deep knowledge of signal strength across your campus or being physically extremely close to an access point, while constantly regulating signal strength.

If an attacker spoofs the BSSID (MAC address) of an existing access point and even synchronizes the beacon interval and MAC timestamps, there is one thing he or she can't hide: The signal strength will be different than the signal strength of the spoofed legitimate access point.

Running a Generate chart analysis on the signal quality field for every legitimate access point MAC address should not show significant changes in signal strength over time because access points usually do not move around.

An attacker would leave a pattern like this, where the mean signal quality for a MAC address suddenly changes:

Graylog query:

Signal strength analysis

(Note that I'm using a workaround here by counting the total frames in a custom chart. Graylog v2.4. will make it possible to chart this directly out of a Quick Values analysis with one click.)

Unusual behavior

Depending on their configuration, some rogue access points will also behave in unusual ways. For example, a rogue access point that tries to lure as many devices as possible blindly will probably listen for frames and always respond with a or frame for the requested SSID. This will lead to patterns you can detect.

One of those patterns is a BSSID that answers in the name of too many SSIDs. Let's take a look at that:

Graylog query:

Unusual behavior

This is way too many and completely unrelated SSIDs for a single access point and most likely a rogue access point in catch-all mode.

(The stacking functionality in Graylog v2.4 will make this much easier and no longer require to pre-select a BSSID. You'll be able to group the number of different SSIDs per BSSID in one analysis.)

Forensics and incident response

When either a WiFi IDS tool like Kismet raised an alarm or your threat hunting found something interesting, you can use the nzyme data in Graylog to respond.

Who connected to a rogue access point?

The WiFi authentication and association process is performed with management frames so we can follow every single step for every single device on channels we are monitoring. All we need is a list of BSSIDs that rogue access point used.

We can find out with a search for all association requests to the rogue access point and then run a Quick Values analysis on the field. This will return a list of all devices that asked to associate with the BSSIDs we think were used by the rogue access point, matching on any SSID in case the attacker was spoofing multiple networks.

Graylog query:

Who connected?

Did a rogue access point target a specific device or person?

With the same list of BSSIDs that an attacker used, we can also find out if a specific device or individual was targeted. For example, if we look at the and frames sent by the rogue access point, we might notice that the target's home network SSID was used, too.

Graylog query:

Targets

Here you can see that multiple SSIDs were used. Pick all the SSIDs that are not from your main networks and research further to figure out if there is a targeting pattern. Protip: You could search for all MAC addresses that were sending for each of those SSIDs and map them to a device or individual.

Red teams

Reconnaissance

The WiFi Pineapple is not only used a lot to spin up rogue access point but also for reconnaissance. You can walk around with a Pineapple Nano in your pocket and collect a complete overview of your target's networks and even devices and what other networks they are looking for.

Someone in the audience at my Derbycon talk had a very interesting idea: You could use nzyme for reconnaissance.

Yagi Antenna

Imagine running a Raspberry Pi in your backpack and walking around the target area or pointing a Yagi antenna at another building and collecting hours or days of data that you can then later analyze in Graylog to plan the next stages of your penetration test.

Try it

Follow the README for installation instructions and download nzyme from GitHub. It should not take longer than 5 minutes to get started.

Please report any issues, questions or feature requests in the issue tracker.

Sours: https://www.wtf.horse/2017/10/02/introducing-nzyme-wifi-802-11-frame-recording-and-forensics/
DragonOS Focal Nzyme v1.0.0 Test w/ 2x LoRa + bladeRF-wiphy (SX126X LoRa, bladeRFxA9, NUC) part 2

Nzyme

Build and TestCodecovLicense

Introduction

Nzyme collects 802.11 management frames directly from the air and sends them to a Graylog (Open Source log management) setup for WiFi IDS, monitoring, and incident response. It only needs a JVM and a WiFi adapter that supports monitor mode.

Think about this like a long-term (months or years) distributed Wireshark/tcpdump that can be analyzed and filtered in real-time, using a powerful UI.

If you are new to the fascinating space of WiFi security, you might want to read my Common WiFi Attacks And How To Detect Them blog post.

A longer blog post with nzyme examples and use-cases is published on my blog:Introducing Nzyme: WiFi Monitoring, Intrusion Detection And Forensics

Picture of a nzyme sensor

What kind of data does it collect?

Nzyme collects, parses and forwards all relevant 802.11 management frames. Management frames are unencrypted so anyone close enough to a sending station (an access point, a computer, a phone, a lightbulb, a car, a juice maker, ...) can pick them up with nzyme.

  • Association request
  • Association response
  • Probe request
  • Probe response
  • Beacon
  • Disassociation
  • Authentication
  • Deauthentication

What do I need to run it?

Everything you need is available from Amazon Prime and is not very expensive. There even is a good chance you have the parts around already.

One or more WiFi adapters that support monitor mode on your operating system.

The most important component is one (or more) WiFi adapters that support monitor mode. Monitor mode is the special state of a WiFi adapter that makes it read and report all 802.11 frames and not only certain management frames or frames of a network it is connected to. You could also call this mode sniffing mode: The adapter just spits out everything it sees on the channel it is tuned to.

The problem is, that many adapter/driver/operating system combinations do not support monitor mode.

The internet is full of compatibility information but here are the adapters I run nzyme with on a Raspberry Pi 3 Model B:

If you have another one that supports monitor mode, you can use that one. Nzyme does not require any specific hardware.

A small computer to run nzyme on.

I recommend to run nzyme on a Raspberry Pi 3 Model B. This is pretty much the reference architecture, because that is what I run it on. A Raspberry Pi 3 Model B running Nzyme with three WiFi adapters in monitor mode has about 25% CPU utilization in the busy frequencies of Downtown Houston, TX.

In the end, it shouldn’t really matter what you run it on, but the docs and guides will most likely refer to a Raspberry Pi with a Raspbian on it.

A Graylog setup

You need a Graylog setup with ah GELF TCP input that is reachable by your nzyme sensors. GELF is a Graylog-specific and structured log format. Because nzyme sends GELF, you don't have to set up any kind of parsing rules in Graylog and still have all fields available as key:value pairs for powerful search and analysis.

You can start a GELF input for nzyme using your Graylog Web Interface. Navigate to System -> Inputs, select GELF TCP in the dropdown menu and hit Launch new input. A modal dialog will open and ask you a few questions about, for example, which address to bind on and what port to use. The input will be immediately available for nzyme after pressing Save.

How to start a Graylog input

Channel hopping

The 802.11 standard defines many frequencies (channels) a network can operate on. This is useful to avoid contention and bandwidth issues, but also means that your wireless adapter has to be tuned to a single channel. During normal operations, your operating system will do this automatically for you.

Because we don’t want to listen on only one, but possibly all WiFi channels, we either need dozens of adapters, with one adapter for each channel, or we cycle over multiple channels on a single adapter rapidly. Nzyme allows you to configure multiple channels per WiFi adapter.

For example, if you configure nzyme to listen on channel 1,2,3,4,5,6 on and 7,8,9,10,11 on , it will tune to channel 1 for a configurable time (default is 1 second) and then switch to channel 2, then to channel 3 and so on. By doing this, we might miss a bunch of wireless frames but are not missing out on some channels completely.

The best configuration depends on your use-case but usually you will want to tune to all 2.4 Ghz and 5 Ghz WiFi channels.

On Linux, you can get a list of channels your WiFi adapter supports like this:

Things to keep in mind

A few general things to know before you get started:

  • Success will highly depend on how well supported your WiFi adapters and drivers are. Use the recommended adapters for best results. You can get them from Amazon Prime and have them ready in one or two days.
  • At least on OSX, your adapter will not switch channels when already connected to a network. Make sure to disconnect from networks before using nzyme with the on-board WiFi adapter. On other systems, switching to monitor mode should disconnect the adapter from a possibly connected network.
  • Nzyme works well with both the OpenJDK or the Oracle JDK and requires Java 7 or 8.
  • Wifi adapters can draw quite some current and I have seen Raspberry Pi 3’s shut down when connecting more than 3 ALFA adapters. Consider this before buying tons of adapters.

Testing on a MacBook

(You can skip this and go straight to a real installation on a Raspberry Pi or install it on any other device that runs Java and has supported WiFi adapters connected to it.)

Requirements

Nzyme is able to put the onboard WiFi adapter of recent MacBooks into monitor mode so you don’t need an external adapter for testing. Remember that you cannot be connected to a wireless network while running nzyme, so the Graylog setup you send data to has to be local or you need a wired network connection or a second WiFi adapter as LAN/WAN uplink.

Make sure you have Java 7 or 8 installed:

Download and configure

Download the most recent build from the Releases page.

Create a new file called in the same folder as your file:

Note the variable that has to point to a GELF TCP input in your Graylog setup. Adapt it accordingly.

Please refer to the example config in the repository for a more verbose version with comments.

Run

After disconnecting from all WiFi networks (you might have to "forget" them in the macOS WiFi settings), you can start nzyme like this:

Nzyme is now collecting data and writing it into the Graylog input you configured. A message will look like this:

Example message in Graylog

Installation and configuration on a Raspberry Pi 3

Requirements

The onboard WiFi chips of recent Raspberry Pi models can be put into monitor mode with the alternative nexmon driver. The problem is, that the onboard antenna is not very good. If possible, use an external adapter that supports monitor mode instead.

Make sure you have Java 7 or 8 installed:

Also install :

Download and configure

Download the most recent Debian package () from the Releases page.

Install the package:

Copy the automatically installed config file:

Change the parameters in the config file to adapt to your WiFi adapters, Graylog GELF input (See What do I need to run it? -> A Graylog setup and use-case. The file should be fairly well documented and self-explanatory.

Now enable the service to make it start on boot of the Raspberry Pi:

Because we are not rebooting, we have to start the service manually for once:

Result of systemctl status

That's it! Nzyme should now be logging into your Graylog setup. Logs can be found in and log rotation is enabled by default. You can change logging and log rotation settings in .

Renaming WiFi interfaces (optional)

The interface names , etc are not always deterministic. Sometimes they can change after a reboot and suddenly nzyme will attempt to use the onboard WiFi chip that does not support monitor mode. To avoid this problem, you can "pin" interface names by MAC address. I like to rename the onboard chip to to avoid accidental usage.

IMPORTANT NOTE: Starting with Debian/Raspbian Stretch (late 2017), started to assign predictable network interface names by default. To enable this on Raspbian, you only have to delete the symlink and restart your Raspberry Pi. After this, you'll see a predictable naming scheme that includes the MAC address of the device. For example, my previously named is now always . Do this and skip all following steps for renaming network interfaces if you are on Debian/Raspbian Stretch. (You can find out your version like this: )

This is what looks like with no external WiFi adapters plugged in.

In this case is the onboard WiFi chip that we want to rename to .

Open the file and add to the device name whitelist:

Reboot the system. After it is back up, open and change the variable:

Reboot the system again and enjoy the consistent naming. Any new WiFi adapter you plug in, will be a classic, numbered , etc that can be safely referenced in the nzyme config without the chance of accidentally selecting the onboard chip, because it's called now.

Known issues

  • Some WiFi adapters will not report the MAC timestamp in the radiotap header. The field will simply be missing in Graylog. This is usually an issue with the driver.
  • Some Linux distributions will try to manage the network adapters for you and interfere with nzyme. For example, on Ubuntu, you have to disable . There is plenty of documentation for this available and I will not duplicate it. I also did not encounter this on any Raspbian based Raspberry Pi yet. The project has a built in way to find and kill processes that might interfere:

If you are running or developing nzyme on a Ubuntu machine, you can exclude your WiFi adapters from management by by configuring this in :

Remember to restart after the change:

  • Running without root rights is possible on many Linux distributions by adding special capabilities to the executable: (Make sure to use the correct path to your specific )

Protips

Use Graylog lookup tables

A simple CSV lookup table for Graylog can translate BSSIDs/MAC addresses to real device names for easier browsing and quicker analysis.

A message with translated fields could look like this:

Enriched message

CLI parameters

Nzyme has a few CLI parameters, some of which can be helpful for debugging.

  • ,
    • Path to config file. This is the only required parameter.
  • ,
    • Override Log4j configuration and start with log level .
  • ,
    • Override Log4j configuration and start with log level .
  • ,
    • Print simple packet size information for every frame that is received.

As an example for CLI parameter usage, here is how to start nzyme in debug mode with packet information printing:

Version Checks

By default, nzyme will check if there is a more recent stable release available by requesting information about the latest release from

Legal notice

Make sure to comply with local laws, especially with regards to wiretapping, when running nzyme. Note that nzyme is never decrypting any data but only reading unencrypted data on license-free frequencies.

Sours: https://opensourcelibs.com/lib/nzyme

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