160 skills found · Page 4 of 6
82goober82 / FluxionwpacrackFluxion is the future of MITM WPA attacks Fluxion is a security auditing and social-engineering research tool. It is a remake of linset by vk496 with (hopefully) less bugs and more functionality. The script attempts to retrieve the WPA/WPA2 key from a target access point by means of a social engineering (phishing) attack. It's compatible with the latest release of Kali (rolling). Fluxion's attacks' setup is mostly manual, but experimental auto-mode handles some of the attacks' setup parameters. Read the FAQ before requesting issues. If you need quick help, fluxion is also avaible on gitter. You can talk with us on Gitter or on Discord. Installation Read here before you do the following steps. Download the latest revision git clone --recursive git@github.com:FluxionNetwork/fluxion.git Switch to tool's directory cd fluxion Run fluxion (missing dependencies will be auto-installed) ./fluxion.sh Fluxion is also available in arch cd bin/arch makepkg or using the blackarch repo pacman -S fluxion scroll Changelog Fluxion gets weekly updates with new features, improvements, and bugfixes. Be sure to check out the changelog here. :octocat: How to contribute All contributions are welcome! Code, documentation, graphics, or even design suggestions are welcome; use GitHub to its fullest. Submit pull requests, contribute tutorials or other wiki content -- whatever you have to offer, it'll be appreciated but please follow the style guide. book How it works Scan for a target wireless network. Launch the Handshake Snooper attack. Capture a handshake (necessary for password verification). Launch Captive Portal attack. Spawns a rogue (fake) AP, imitating the original access point. Spawns a DNS server, redirecting all requests to the attacker's host running the captive portal. Spawns a web server, serving the captive portal which prompts users for their WPA/WPA2 key. Spawns a jammer, deauthenticating all clients from original AP and lureing them to the rogue AP. All authentication attempts at the captive portal are checked against the handshake file captured earlier. The attack will automatically terminate once a correct key has been submitted. The key will be logged and clients will be allowed to reconnect to the target access point. For a guide to the Captive Portal attack, read the Captive Portal attack guide exclamation Requirements A Linux-based operating system. We recommend Kali Linux 2 or Kali rolling. Kali 2 & rolling support the latest aircrack-ng versions. An external wifi card is recommended. Related work For development I use vim and tmux. Here are my dotfiles :octocat: Credits l3op - contributor dlinkproto - contributor vk496 - developer of linset Derv82 - @Wifite/2 Princeofguilty - @webpages and @buteforce Photos for wiki @http://www.kalitutorials.net Ons Ali @wallpaper PappleTec @sites MPX4132 - Fluxion V3 Disclaimer Authors do not own the logos under the /attacks/Captive Portal/sites/ directory. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. The usage of Fluxion for attacking infrastructures without prior mutual consent could be considered an illegal activity, and is highly discouraged by its authors/developers. It is the end user's responsibility to obey all applicable local, state and federal laws. Authors assume no liability and are not responsible for any misuse or damage caused by this program. Note Beware of sites pretending to be related with the Fluxion Project. These may be delivering malware. Fluxion DOES NOT WORK on Linux Subsystem For Windows 10, because the subsystem doesn't allow access to network interfaces. Any Issue regarding the same would be Closed Immediately Links Fluxion website: https://fluxionnetwork.github.io/fluxion/ Discord: https://discordapp.com/invite/G43gptk Gitter: https://gitter.im/FluxionNetwork/Lobby
bglezseoane / GitcherThe git profile switcher
jnicklas / PartnersSwitch between different git users
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loclink / GlisterThe git user switch tool, user management tool : git终端交互式管理工具，git用户管理工具
TheDevOpsBlueprint / Github Account Switch CliA fast, reliable cli tool for managing multiple GitHub accounts.
AlexandraKreidich / SwitchGithubAccountsChrome extension
danielbachhuber / Git SwitchSwitch your theme between remote Git branches
YTakahashii / PortmuxA developer CLI that ends port conflicts. Manages process groups with Git worktree-aware port isolation for parallel development and seamless context switching.
jillesme / GrecentGit branch switcher from the command line, for recently used branches
3rd / WorkConvenient wrapper over git-worktree, using fzf for switching between trees and branches.
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
sonatard / Git Switch TrainerYou are forced to use git switch and git restore instead of git checkout by `git-switch-trainer`.
ltxzs / Opencode Github Copilot Switcher用于在opencode中切换github账号,A tool to switch GitHub Copilot accounts in OpenCode
Heroinru / Https Github.com Rocknrostov SoundSwitchNo description available
takuma7 / Git ProfileA CLI tool to manage and easily switch between multiple git profiles
withshubh / Ggusergguser is a CLI tool to easily switch between different Git user profiles.
olmokramer / Atom Tree View Git BranchSwitch git branches from the tree view
chrisdicarlo / Vscode Switch Git WorktreeEasily switch between Git worktrees
helmecke / Telescope Git Worktree.nvimA telescope.nvim extension that offers switching worktrees in a bare git repository.
