Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Python Web Penetration Testing Cookbook

You're reading from   Python Web Penetration Testing Cookbook Over 60 indispensable Python recipes to ensure you always have the right code on hand for web application testing

Arrow left icon
Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781784392932
Length 224 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (4):
Arrow left icon
Cameron Buchanan Cameron Buchanan
Author Profile Icon Cameron Buchanan
Cameron Buchanan
Terry Ip Terry Ip
Author Profile Icon Terry Ip
Terry Ip
Andrew Mabbitt Andrew Mabbitt
Author Profile Icon Andrew Mabbitt
Andrew Mabbitt
Benjamin May Benjamin May
Author Profile Icon Benjamin May
Benjamin May
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Python Web Penetration Testing Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Gathering Open Source Intelligence FREE CHAPTER 2. Enumeration 3. Vulnerability Identification 4. SQL Injection 5. Web Header Manipulation 6. Image Analysis and Manipulation 7. Encryption and Encoding 8. Payloads and Shells 9. Reporting Index

Checking jitter


The only difficult thing about performing time-based SQL Injections is that plague of gamers everywhere, lag. A human can easily sit down and account for lag mentally, taking a string of returned values, and sensibly going over the output and working out that cgris is chris. For a machine, this is much harder; therefore, we should attempt to reduce delay.

We will be creating a script that makes multiple requests to a server, records the response time, and returns an average time. This can then be used to calculate fluctuations in responses in time-based attacks known as jitter.

How to do it…

Identify the URLs you wish to attack and provide to the script through a sys.argv variable:

import requests
import sys
url = sys.argv[1]

values = []

for i in xrange(100): 
  r = requests.get(url)
  values.append(int(r.elapsed.total_seconds()))

average = sum(values) / float(len(values))
print “Average response time for “+url+” is “+str(average)

The following screenshot is an example of the...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £13.99/month. Cancel anytime
Visually different images