Bug Bounty Top 10 Most Impactful and Rewarded Vulnerability Types
https://amolrangari.medium.com/top-10-most-impactful-and-rewarded-vulnerability-types-8c9373d150d?source=rss------bug_bounty-5

Top 10 Most Impactful and Rewarded Vulnerability Types:Continue reading on Medium » (https://amolrangari.medium.com/top-10-most-impactful-and-rewarded-vulnerability-types-8c9373d150d?source=rss------bug_bounty-5)

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Simple logical Bug turned into a bounty
https://sndpgiriz.medium.com/simple-logical-bug-turned-into-a-bounty-a3d7ac214606?source=rss------bug_bounty-5

Hello all,Continue reading on Medium » (https://sndpgiriz.medium.com/simple-logical-bug-turned-into-a-bounty-a3d7ac214606?source=rss------bug_bounty-5)

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Deep Web
I want to get into the deep web

[removed]

submitted by /u/LatrinaWimbush
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FreeCourseSite – Download Udemy Paid Courses For Free
Designing & Building MS Endpoint Configuration Manager SCCM

Designing & Building MS Endpoint Configuration Manager SCCM
Microsoft SCCM
What you’ll learn

Designing & Building MS Endpoint Configuration Manager SCCM
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Install an Endpoint Configuration Manager Site Server
*

Install the Client Agent
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Build roles including Management Points, Distribution Points, and Software Update Points
*

Manage distribution bandwidth
*

Support internet-based clients via IBCM, CDP, and CMG
*

Implement an enterprise-scale site structure
*

Perform maintenance and backup
Requirements

*

Knowledge of Windows Server, Active Directory, Basic Networking Skills, Endpoint Configuration Manager administration skills
Description
With nearly 10,000 training videos available for desktop applications, technical concepts, and business skills that comprise hundreds of courses, Intellezy has many of the videos and courses you and your workforce need to stay relevant and take your skills to the next level. Our video content is engaging and offers assessments that can be used to test knowledge levels pre and/or post-course.
Our training content is also frequently refreshed to keep current with changes in the software. This ensures you and your employees get the most up-to-date information and techniques for success. And, because our video development is in-house, we can adapt quickly and create custom content for a more exclusive approach to software and computer system roll-outs.

In this course, you will learn how to build an Endpoint Configuration Manager environment from scratch. Provide role servers that meet your performance-based design goals. Meet the needs of an enterprise by building sites. Extend the power of Endpoint Configuration Manager by supporting external internet-based clients.

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*
Last updated 2/2021
Content From: https://www.udemy.com/course/designing-building-ms-endpoint-configuration-manager-sccm/
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FreeCourseSite – Download Udemy Paid Courses For Free
The Complete Data Structures and Algorithms Course in Python

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Free Course Site
Progressive Web Apps (PWA) – The Complete Guide

https://freecoursesite.com/wp-content/uploads/2018/01/1329100_571a.jpg
Build a Progressive Web App (PWA) that feels like an iOS & Android App, using Device Camera, Push Notifications and more What you’ll learn Build web apps that look and feel like native mobile apps for iOS and Android Use service workers to build web apps that work without internet connection (offline-first) Leverage device features […]

The post Progressive Web Apps (PWA) – The Complete Guide appeared first on Free Course Site.
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test trends in the industry
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• Get ready to fall in love with Design and making everything you touch into beautiful projects for the rest of your life! Who is this course for?• You are a complete beginner and want to become a successful designer or freelancer
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The post [AcademyZeroToMastery] Complete Web & Mobile Designer in 2021: UI/UX, Figma + more appeared first on Course Club.

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his course.
* Any student who has a basic understanding of Laravel Project structure, Migrations, routes, models, and controllers can follow through this course.
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mature cybersec learning
https://www.reddit.com/r/Pentesting/comments/n92obo/mature_cybersec_learning/

I have decieded to make a Discord Server for mature/chill people to learn cybersec. The recent discord servers people have been making have either been getting banned due to illegal requests/activities or just plain chaos with no moderation. Im looking to make a small community where people can go to be surrounded by friendly and helping people. We welcome people of all skill ranges from complete begginer to expert. come have a look around: https://discord.gg/EvxPjXv4U6 submitted by /u/FSX-94 (https://www.reddit.com/user/FSX-94)
[link] (https://www.reddit.com/r/Pentesting/comments/n92obo/mature_cybersec_learning/) [comments] (https://www.reddit.com/r/Pentesting/comments/n92obo/mature_cybersec_learning/)

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hacking: security in practice
Linux Flash Drive

I'm thinking about using a 250GB flashdrive to store Kali Linux to practice penetration testing and such and also to do other things like browse youtube on Tor (lol). I have to use MicroTrash 10 for school work but I only have 1 laptop. Is this a bad idea in regards to cyber security since the second reason I'm using linux is for privacy and security of my data?

I couldn't find a vulnerability

submitted by /u/UnspecifiedCow
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hacking: security in practice
HackerOne

Is HackerOne a good website to start?

submitted by /u/No_Succotash4594
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hacking: security in practice
Changing Location.

I have an online exam and want to change my location so the provider can not see where I live. They must not be able to see that I use VPN. How can I do this?

submitted by /u/Standard007
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CyberBattleSim - An Experimentation And Research Platform To Investigate The Interaction Of Automated Agents In An Abstract Simulated Network Environments
http://www.kitploit.com/2021/05/cyberbattlesim-experimentation-and.html

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CyberBattleSim is an experimentation research platform to investigate the interaction of automated agents operating in a simulated abstract enterprise network environment. The simulation provides a high-level abstraction of computer networks and cyber security concepts. Its Python-based Open AI Gym interface allows for training of automated agents using reinforcement learning algorithms. The simulation environment is parameterized by a fixed network topology and a set of vulnerabilities (https://www.kitploit.com/search/label/vulnerabilities) that agents can utilize to move laterally in the network. The goal of the attacker is to take ownership of a portion of the network by exploiting vulnerabilities that are planted in the computer nodes. While the attacker attempts to spread throughout the network, a defender agent watches the network activity and tries to detect any attack taking place and mitigate the impact on the system by evicting the attacker. We provide a basic stochastic defender that detects and mitigates ongoing attacks based on pre-defined probabilities of success. We implement mitigation by re-imaging the infected nodes, a process abstractly modeled as an operation spanning over multiple simulation steps. To compare the performance of the agents we look at two metrics: the number of simulation steps taken to attain their goal and the cumulative rewards over simulation steps across training epochs.
Project goals
We view this project as an experimentation platform to conduct research on the interaction of automated agents in abstract simulated network environments. By open sourcing it we hope to encourage the research community to investigate how cyber-agents interact and evolve in such network environments. The simulation we provide is admittedly simplistic, but this has advantages. Its highly abstract nature prohibits direct application to real-world systems thus providing a safeguard against potential nefarious use of automated agents trained with it. At the same time, its simplicity allows us to focus on specific security aspects we aim to study and quickly experiment with recent machine learning (https://www.kitploit.com/search/label/Machine%20Learning) and AI algorithms. For instance, the current implementation focuses on the lateral movement (https://www.kitploit.com/search/label/Lateral%20Movement) cyber-attacks techniques, with the hope of understanding how network topology and configuration affects them. With this goal in mind, we felt that modeling actual network traffic was not necessary. This is just one example of a significant limitation in our system that future contributions might want to address. On the algorithmic side, we provide some basic agents as starting points, but we would be curious to find out how state-of-the art reinforcement learning algorithms compare to them. We found that the large action space intrinsic to any computer system is a particular challenge for Reinforcement Learning, in contrast to other applications such as video games or robot control. Training agents that can store and retrieve credentials (https://www.kitploit.com/search/label/Credentials) is another challenge faced when applying RL techniques where agents typically do not feature internal memory. These are other areas of research where the simulation could be used for benchmarking (https://www.kitploit.com/search/label/Benchmarking) purposes. Other areas of interest include the responsible and ethical use of autonomous cyber-security systems: How to design an enterprise network that gives an intrinsic advantage to defender agents? How to conduct safe research aimed at defending enterprises against autonomous cyber-attacks while preventing nefarious use of such technology?
Documentation
Read the Quick introduction (https://github.com/microsoft/CyberBattleSim/blob/main/docs/quickintro.md) to the project.
Benchmark

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See Benchmark (https://github.com/microsoft/CyberBattleSim/blob/main/docs/benchmark.md).
Setting up a dev environment
It is strongly recommended to work under a Linux environment, either directly or via WSL on Windows. Running Python on Windows directly should work but is not supported anymore. Start by checking out the repository: git clone https://github.com/microsoft/CyberBattleSim.git
On Linux or WSL
The instructions were tested on a Linux Ubuntu distribution (both native and via WSL). Run the following command to set-up your dev environment and install all the required dependencies (apt and pip packages): ./init.sh The script installs python3.8 if not present. If you are running a version of Ubuntu older than 20 it will automatically add an additional apt repository to install python3.8. The script will create a virtual Python environment (https://docs.python.org/3/library/venv.html) under a venv subdirectory, you can then run Python with venv/bin/python. Note: If you prefer Python from a global installation instead of a virtual environment then you can skip the creation of the virtual envrionment by running the script with ./init.sh -n. This will instead install all the Python packages on a system-wide installation of Python 3.8.
Windows Subsystem for Linux
The supported dev environment on Windows is via WSL. You first need to install an Ubuntu WSL distribution on your Windows machine, and then proceed with the Linux instructions (next section).
Git authentication from WSL
To authenticate with Git you can either use SSH-based authentication, or alternatively use the credential-helper trick to automatically generate a PAT token. The latter can be done by running the following commmand under WSL (more info here (https://docs.microsoft.com/en-us/windows/wsl/tutorials/wsl-git)): git config --global credential.helper "/mnt/c/Program\ Files/Git/mingw64/libexec/git-core/git-credential-manager.exe"
Docker on WSL
To run your environment within a docker container, we recommend running docker via Windows Subsystem on Linux (WSL) using the following instructions: Installing Docker on Windows under WSL (https://docs.docker.com/docker-for-windows/wsl-tech-preview/)).
Windows (unsupported)
This method is not maintained anymore, please prefer instead running under a WSL subsystem Linux environment. But if you insist you want to start by installing Python 3.8 (https://www.python.org/downloads/windows/) then in a Powershell prompt run the ./init.ps1 script.
Getting started quickly using Docker
The quickest method to get up and running is via the Docker container. NOTE: For licensing reasons, we do not publicly redistribute any build artifact. In particular the docker registry spinshot.azurecr.io referred to in the commands below is kept private to the project maintainers only. As a workaround, you can recreate the docker image yourself using the provided Dockerfile, publish the resulting image to your own docker registry and replace the registry name in the commands below. commit=7c1f8c80bc53353937e3c69b0f5f799ebb2b03ee
docker login spinshot.azurecr.io
docker pull spinshot.azurecr.io/cyberbattle:$commit
docker run -it spinshot.azurecr.io/cyberbattle:$commit cyberbattle/agents/baseline/run.py
Check your environment
Run the following command to run a simulation with a baseline RL agent: python cyberbattle/agents/baseline/run.py --training_episode_count 1 --eval_episode_count 1 --iteration_count 10 --rewardplot_with 80 --chain_size=20 --ownership_goal 1.0
If everything is setup correctly you should get an output that looks like this: torch cuda available=True
###### DQL
Learning with: episode_count=1,iteration_count=10,ϵ=0.9,ϵ_min=0.1, ϵ_expdecay=5000,γ=0.015, lr=0.01, replaymemory=10000,
batch=512, target_update=10
## Episode: 1/1 'DQL' ϵ=0.9000, γ=0.015, lr=0.01, replaymemory=10000,
batch=512, target_update=10

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Episode 1|Iteration 10|reward: 139.0|Elapsed Time: 0:00:00|###################################################################|
###### Random search
Learning with: episode_count=1,iteration_count=10,ϵ=1.0,ϵ_min=0.0,
## Episode: 1/1 'Random search' ϵ=1.0000,
Episode 1|Iteration 10|reward: 194.0|Elapsed Time: 0:00:00|###################################################################|
simulation ended
Episode duration -- DQN=Red, Random=Green
10.00 ┼
Cumulative rewards -- DQN=Red, Random=Green
194.00 ┼ ╭──╴
174.60 ┤ │
155.20 ┤╭─────╯
135.80 ┤│ ╭──╴
116.40 ┤│ │
97.00 ┤│ ╭╯
77.60 ┤│ │
58.20 ┤╯ ╭──╯
38.80 ┤ │
19.40 ┤ │
0.00 ┼──╯
Jupyter notebooks
To quickly get familiar with the project you can open one the the provided Juptyer notebooks to play interactively with the gym environments. Just start jupyter with jupyter notebook, or venv/bin/jupyter notebook if you are using a virtual environment setup. 'Capture The Flag' toy environment notebooks: Random agent (https://github.com/microsoft/CyberBattleSim/blob/main/notebooks/toyctf-random.ipynb) Interactive session for a human player (https://github.com/microsoft/CyberBattleSim/blob/main/notebooks/toyctf-blank.ipynb) Interactive session - fully solved (https://github.com/microsoft/CyberBattleSim/blob/main/notebooks/toyctf-solved.ipynb) Chain environment notebooks: Random agent (https://github.com/microsoft/CyberBattleSim/blob/main/notebooks/chainnetwork-random.ipynb) Other environments: Interactive session with a randomly generated environment (https://github.com/microsoft/CyberBattleSim/blob/main/notebooks/randomnetwork.ipynb) Random agent playing on randomly generated networks (https://github.com/microsoft/CyberBattleSim/blob/main/notebooks/c2_interactive_interface.ipynb) The following .py notebooks are best viewed in VSCode or in Jupyter with the Jupytext extension (https://jupytext.readthedocs.io/en/latest/install.html) and can easily be converted to .ipynb format if needed: Chain environments benchmarks: Benchmark of all baseline agents (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/agents/baseline/notebooks/notebook_all_agents_benchmark.py) All baseline agents against a basic defender (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/agents/baseline/notebooks/notebook_withdefender.py) DeepQL (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/agents/baseline/notebooks/notebook_dql.py) Epsilon greedy (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/agents/baseline/notebooks/notebook_randlookups.py) Tabular Q Learning (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/agents/baseline/notebooks/notebook_tabularq.py) Capture the Flag benchmark: DeepQL (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/agents/baseline/notebooks/notebook_ctf_dql.py)
How to instantiate the Gym environments?
The following code shows how to create an instance of the the OpenAI Gym environment CyberBattleChain-v0, an environment based on a chain-like network structure (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/samples/chainpattern/chainpattern.py), with 10 nodes (size=10) where the agent's goal is to either gain full ownership of the network (own_atleast_percent=1.0) or break the 80% network availability SLA (maintain_sla=0.80), while the netowrk is being monitored and protected by basic probalistically-modelled defender (defender_agent=ScanAndReimageCompromisedMachines): import cyberbattle._env.cyberbattle_env

cyberbattlechain_defender =
gym.make('CyberBattleChain-v0',
size=10,
attacker_goal=AttackerGoal(
own_atleast=0,
own_atleast_percent=1.0
),
defender_constraint=DefenderConstraint(
maintain_sla=0.80
),
defender_agent=ScanAndReimageCompromisedMachines(

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probability=0.6,
scan_capacity=2,
scan_frequency=5)) To try other network topologies, take example on chainpattern.py (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/samples/chainpattern/chainpattern.py) to define your own set of machines and vulnerabilities, then add an entry in the module initializer (https://github.com/microsoft/CyberBattleSim/blob/main/cyberbattle/__init__.py) to declare and register the Gym environment.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com (https://cla.opensource.microsoft.com/). When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the Microsoft Open Source Code of Conduct (https://opensource.microsoft.com/codeofconduct/). For more information see the Code of Conduct FAQ (https://opensource.microsoft.com/codeofconduct/faq/) or contact opencode@microsoft.com (mailto:opencode@microsoft.com) with any additional questions or comments.
Ideas for contributions
Here are some ideas on how to contribute: enhance the simulation (event-based, refined the simulation, …), train an RL algorithm on the existing simulation, implement benchmark to evaluate and compare novelty of agents, add more network generative modes to train RL-agent on, contribute to the doc, fix bugs. See also the wiki for more ideas (https://github.com/microsoft/CyberBattleGym/wiki/Possible-contributions).
Citing this project
@misc{msft:cyberbattlesim,
Author = {Microsoft Defender Research Team.}
Note = {Created by Christian Seifert, Michael Betser, William Blum, James Bono, Kate Farris, Emily Goren, Justin Grana, Kristian Holsheimer, Brandon Marken, Joshua Neil, Nicole Nichols, Jugal Parikh, Haoran Wei.},
Publisher = {GitHub},
Howpublished = {\url{https://github.com/microsoft/cyberbattlesim}},
Title = {CyberBattleSim},
Year = {2021}
}
Note on privacy
This project does not include any customer data. The provided models and network topologies are purely fictitious. Users of the provided code provide all the input to the simulation and must have the necessary permissions to use any provided data.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines (https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Download CyberBattleSim (https://github.com/microsoft/CyberBattleSim)

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