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Effective Python Penetration Testing

Effective Python Penetration Testing

By : Rejah Rehim
4 (1)
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Effective Python Penetration Testing

Effective Python Penetration Testing

4 (1)
By: Rejah Rehim

Overview of this book

Penetration testing is a practice of testing a computer system, network, or web application to find weaknesses in security that an attacker can exploit. Effective Python Penetration Testing will help you utilize your Python scripting skills to safeguard your networks from cyberattacks. We will begin by providing you with an overview of Python scripting and penetration testing. You will learn to analyze network traffic by writing Scapy scripts and will see how to fingerprint web applications with Python libraries such as ProxMon and Spynner. Moving on, you will find out how to write basic attack scripts, and will develop debugging and reverse engineering skills with Python libraries. Toward the end of the book, you will discover how to utilize cryptography toolkits in Python and how to automate Python tools and libraries.
Table of Contents (11 chapters)
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Get the EXIF data of an image

We can find a lot of information from an image posted online. For every photo we took with our smartphone or camera, it records the date, time, shutter speed, aperture setting, ISO setting, whether the flash was used, the focal length, and lots more. This is stored with the photo, and is referred to as EXIF data. When we copy an image, the EXIF data is copied as well, as a part of the image. It can pose a privacy issue. For instance, a photo taken with a GPS-enabled phone, it can reveal the location and time it was taken, as well as the unique ID number of the device:

import os,sys 
 
from PIL import Image 
 
from PIL.ExifTags import TAGS 
 
 
 
for (i,j) in Image.open('image.jpg')._getexif().iteritems(): 
 
        print '%s = %s' % (TAGS.get(i), j) 

First we imported the modules PIL image and PIL TAGS. PIL is an image processing module in Python. It supports many file formats and has a powerful image-processing capability. Then we...

Visually different images
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