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About lightoyou

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  1. Hey Guys, Almost everything is in the Title So offsectraining's PWK v2 is out... PDF: - v1: 380 pages - v2: 853 pages Videos - v1: 8+ hours - v2: 17+ hours Targets: - v1: 50+ - v2: 75+ So anyone can share the new version ?
  2. Up I'm student and really need this.. I know people say hey repload please but look very nice ! Hope someone share
  3. Yes please and without a backdoor
  4. and nobody has the ISO 27010:2015 ? I think it's a good Idea to make a repository with all the 27K ISO family
  5. Hello, I search some course like this one under bigdata/machine learning for InfoSec We are now living in a Big Data world - billions of devices communicating over millions of networks and generating petabytes of data, both at rest and in transit! Security professionals now encounter Big Data in the form of large log files, network traffic captures, forensics of large images and exports from security tools and products. In this course, we will look at how to analyze, mangle, transform and visualize data to derive interesting insights and intelligence from it. Pandas is a Python library which is part of SciPy scientific computing ecosystem. In simple terms, Pandas provides powerful data structures to perform data analysis. As dry as this might initially sound, due to the high level of abstraction provided by its powerful API, Pandas allows us to do really complicated analysis with just a few lines of Python code. In this course, we will go through the basics of Numpy, a deep dive into Pandas Series and Dataframes and how to analyze data with it. The case study used is analysis of Wi-Fi networks using Airodump-NG’s output file for a relatively large network with hundreds of devices. A non-exhaustive list of topics covered include: Why Pandas for Pentesters? Lab Setup - Python, Anaconda, Jupyter Numpy basics Pandas Series Vector, Logical, String Operations Pandas Dataframes Filters, Operations, Apply Groupby, Split-Apply-Combine Aggregate, Transform, Filter Airodump-NG Scan Data Access Point Analysis Client Analysis Data Visualization Conclusion [Hidden Content] [Hidden Content] Thank you for your answers
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