Yeah Hub

Yeah Hub

1552311232

Advanced Hacking Tutorials Collection

The world’s most advanced ethical hacking tutorials bookmark compilation.

These tutorials are best place to start and is dedicated to those who are in need of learn from beginners to advanced.

  1. Get Free Kali Linux on AWS with Public IP – Real Time Penetration Testing
  2. Detection and Exploitation of OpenSSL Heartbleed Vulnerability using NMAP and METASPLOIT
  3. Hack Android using Metasploit over LAN/WAN
  4. Generate 100% FUD Backdoor with TheFatRat – Windows 10 Exploitation
  5. SSLKILL – Forced Man in the Middle Attack – Sniff HTTPS/HTTP
  6. Hack Android using Metasploit without Port Forwarding over Internet – 2017
  7. Hack Windows 10 Remotely over WAN with Metasploit [No Port Forwarding]
  8. Hack Windows 10 using CHAOS Framework – 100% FUD
  9. Persistent Backdoor in Android using Kali Linux with a Shell script
  10. Crack WPA/WPA2-PSK using Aircrack-ng and Hashcat – 2017
  11. Hack a website with Ngrok, Msfvenom and Metasploit Framework
  12. Creating an undetectable payload using Veil-Evasion Toolkit
  13. Sniff HTTPS/FTP Packets using SSLSTRIP and DSNIFF – ARP Spoofing MITM Attack
  14. Hack Windows/Linux using ARCANUS Framework – 100% FUD
  15. Live SQL Injection Exploitation with SQLMap – A Detailed Guide
  16. Change Windows Password of Remote PC via METASPLOIT
  17. Advanced Error Based SQL Injection Exploitation – Manually
  18. Automated MITM Attack with MitmAP Python Script
  19. DKMC – Another Wonderful Malicious Payload Evasion Tool (Windows Hacking)
  20. Bypass Hidden SSID in a Wireless Network [Full Proof Method]
  21. Wi-Fi deauthentication attack against 802.11 protocol
  22. Pentesting Windows 2000/2003 Server with Metasploit Framework – Detailed Tutorial
  23. SEToolkit – Credential Harvester Attack [Tutorial]
  24. Crack WPA2-PSK with Aircrack – Dictionary Attack Method
  25. HTTP PUT Method Exploitation – Live Penetration Testing
  26. MySQL Pentesting with Metasploit Framework
  27. PHP CGI Argument Injection With Metasploit Framework
  28. Man in the Middle Attack with Websploit Framework
  29. DDOS a WiFi Network with MDK3 Tool in Kali Linux
  30. Sniffing with Xerosploit – An Advanced MITM Framework
  31. Privilege Escalation via SQL Injection in Joomla 3.8.3 – Live Exploitation
  32. Live Detection and Exploitation of WordPress xmlrpc.php File
  33. [Exploitation] Apache Struts OGNL Code Execution Vulnerability – CVE-2017-9791
  34. Perform DOS Attack with 5 Different Tools – 2018 Update
  35. Windows 10 Exploitation with an Image [Metasploit Framework – 2018]
  36. [Code Execution] – preg_replace() PHP Function Exploitation
  37. WEP Cracking with Kali Linux 2018.1 [Tutorial]
  38. Exploitation of EternalBlue DoublePulsar [Windows 7 – 64bit] with Metasploit Framework
  39. Sniffing with Rogue Access Point [DNSMASQ and TCPFLOW]
  40. Create a Fake AP with DNSMASQ and HOSTAPD [Kali Linux]
  41. Exploit Windows with Malicious MS-OFFICE File [Metasploit Framework]
  42. Host Header Attack – Practical Exploitation and Prevention
  43. Node.js Deserialization Attack – Detailed Tutorial 2018
  44. Apache Java Struts2 Rest Plugin Exploitation – CVE-2017–9805
  45. Exploitation of WPA/WPA2-PSK with WiFiBroot – Kali Linux 2018
  46. File Upload Exploitation and Its Prevention – Detailed Guide 2018
  47. ShellShock Vulnerability Exploitation With Metasploit Framework
  48. ShellShock Vulnerability Exploitation With HTTP Request
  49. Linux Privilege Escalation With Kernel Exploit – [8572.c]
  50. Exploitation of ShellShock Vulnerability with BadBash Tool
  51. ShellShock Exploitation with BurpSuite [PentesterLab] – CVE-2014-6271
  52. ShellShock and BeEF Framework – Exploitation Tutorial
  53. Post Exploitation with PowerShell Empire 2.3.0 [Detailed Tutorial]
  54. Drupal 7 Exploitation with Metasploit Framework [SQL Injection]
  55. Evil Twin Attack with DNSMASQ – Wireless WPA2-PSK Cracking
  56. Exploitation of Opendreambox – Remote Code Execution
  57. Privilege Escalation with PowerShell Empire and SETOOLKIT [Kali Linux]
  58. Exploitation of DVR Cameras – CVE-2018-9995 [Tutorial]
  59. HTTP PUT Method Exploitation with Put2Win (Meterpreter Shell)
  60. From Command Injection To Meterpreter Shell – Detailed Tutorial 2018
  61. JAVA RMI (Remote Method Invocation) Exploitation with Metasploit Framework
  62. From RFI(Remote File Inclusion) to Meterpreter Shell
  63. From Command Execution To Meterpreter Reverse Shell with Commix
  64. [RCE] Exploitation of Microsoft Office/WordPad – CVE-2017-0199 [Tutorial]
  65. Exploitation of UnreaIIRCd 3.2.8.1 by using Metasploit and Perl Script


#php #python

What is GEEK

Buddha Community

Joseph  Murray

Joseph Murray

1621559580

Collection vs Collections in Java: Difference Between Collection & Collections in Java

Introduction

This article will be looking into one of the most popular questions in Java Language – What is Collection in Java? Also, what do you mean by Collections in Java? Are Collection and Collections the same or different in Java?

What is Collection?

What is Collections?

Conclusion

#full stack development #collection #collection vs collections in java #collections in java #difference between collection and collections in java

Willie  Beier

Willie Beier

1596728880

Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

Table of Contents

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

Tutorial: Loading and Cleaning Data with R and the tidyverse

1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

  • Free of duplicate rows/values
  • Error-free (e.g. free of misspellings)
  • Relevant (e.g. free of special characters)
  • The appropriate data type for analysis
  • Free of outliers (or only contain outliers have been identified/understood), and
  • Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

  • Special characters (e.g. commas in numeric values)
  • Numeric values stored as text/character data types
  • Duplicate rows
  • Misspellings
  • Inaccuracies
  • White space
  • Missing data
  • Zeros instead of null values

2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

  • rollingsales_bronx.xls
  • rollingsales_brooklyn.xls
  • rollingsales_manhattan.xls
  • rollingsales_queens.xls
  • rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

3. Load Data into R with readxl

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse readr package function read_csv() is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the readxlpackage. We specify the function argument skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)

Note we saved this dataset with the variable name brooklyn for future use.

4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the glimpse() function that is part of the tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

install.packages("tidyverse")

Once the package is installed, load it to memory:

library(tidyverse)

Now that tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:

glimpse(brooklyn)
## Observations: 20,185
## Variables: 21
## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA…
## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 …
## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…

The glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials

Jeromy  Lowe

Jeromy Lowe

1599097440

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Autumn  Blick

Autumn Blick

1596584126

R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the dyplrlubridate, and stringr packages installed — which, as a reminder, you can do with the install.packages() command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

post_url: the page path of the blog post

post_date: the date the post was published (formatted m/d/yy)

category: the blog category the post was published in (optional)

title: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials