Metasploit
Pandas
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Metasploit | Pandas | |
---|---|---|
117 | 393 | |
32,532 | 41,678 | |
1.2% | 1.6% | |
10.0 | 10.0 | |
7 days ago | 3 days ago | |
Ruby | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Metasploit
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Best Hacking Tools for Beginners 2024
Metasploit
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Metasploit explained for pentesters
msf6 > use auxiliary/scanner/smb/smb_ms17_010 msf6 auxiliary(scanner/smb/smb_ms17_010) > options Module options (auxiliary/scanner/smb/smb_ms17_010): Name Current Setting Required Description ---- --------------- -------- ----------- CHECK_ARCH true no Check for architecture on vulnerable hosts CHECK_DOPU true no Check for DOUBLEPULSAR on vulnerable hosts CHECK_PIPE false no Check for named pipe on vulnerable hosts NAMED_PIPES /usr/share/metasploit-framework/data/wordl yes List of named pipes to check ists/named_pipes.txt RHOSTS yes The target host(s), see https://github.com/rapid7/metasploit-framework/wiki/U sing-Metasploit RPORT 445 yes The SMB service port (TCP) SMBDomain . no The Windows domain to use for authentication SMBPass no The password for the specified username SMBUser no The username to authenticate as THREADS 1 yes The number of concurrent threads (max one per host)
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Effective Adversary Emulation
Metasploit: https://github.com/rapid7/metasploit-framework
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Hacking from anywhere
1-) Learn Hacking on a debian based distro like Kali Linux - I personally started with tools like nikto, camhacker... and then moved to more complex frameworks like metasploit.
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Hackers Tools: Must-Have Tools for Every Ethical Hacker
Metasploit Framework (mentioned earlier)
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I watched a video of Mr. Robot programming a script. As I watch the script, the syntax is reminiscent of the Ruby language, and it really is.
It's using the metasploit framework https://github.com/rapid7/metasploit-framework
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The 36 tools that SaaS can use to keep their product and data safe from criminal hackers (manual research)
Metasploit
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Why are there so many Rails related posts here?
This is something that kind of annoys me; there's even a /r/rails sub-reddit specifically for Ruby on Rails stuff. Understandably Rails helped put Ruby on the map. Before Rails, Ruby was just another fringe language. Rails became massively popular, helped many startups quickly build their Web 2.0 sites, and become successful companies (ex: GitHub, LinkedIn, AirBnB, etc). Like others have said, "Rails is where the money is at". However, this posses a problem for the Ruby community: whenever Rails becomes less popular, so does Ruby. I wish the Ruby ecosystem wasn't so heavily centralized around Rails, and that we diversified our uses of Ruby a bit. There's of course Sinatra, dry-rb, Hanami, Dragon Ruby, SciRuby, and a dozen security tools written in Ruby such as Metasploit, BeFF, Arachni, and Ronin.
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Pentesting Tools I Use Everyday
Learn more about Metasploit here: https://www.metasploit.com/
Pandas
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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How to Build and Deploy a Machine Learning model using Docker
Pandas
What are some alternatives?
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
tensorflow - An Open Source Machine Learning Framework for Everyone
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
BeEF - The Browser Exploitation Framework Project
Keras - Deep Learning for humans
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
SymPy - A computer algebra system written in pure Python
Dask - Parallel computing with task scheduling
NumPy - The fundamental package for scientific computing with Python.
Covenant - Covenant is a collaborative .NET C2 framework for red teamers.