Book Image

Python for Security and Networking - Third Edition

By : José Ortega
4 (3)
Book Image

Python for Security and Networking - Third Edition

4 (3)
By: José Ortega

Overview of this book

Python’s latest updates add numerous libraries that can be used to perform critical security-related missions, including detecting vulnerabilities in web applications, taking care of attacks, and helping to build secure and robust networks that are resilient to them. This fully updated third edition will show you how to make the most of them and improve your security posture. The first part of this book will walk you through Python scripts and libraries that you’ll use throughout the book. Next, you’ll dive deep into the core networking tasks where you will learn how to check a network’s vulnerability using Python security scripting and understand how to check for vulnerabilities in your network – including tasks related to packet sniffing. You’ll also learn how to achieve endpoint protection by leveraging Python packages along with writing forensics scripts. The next part of the book will show you a variety of modern techniques, libraries, and frameworks from the Python ecosystem that will help you extract data from servers and analyze the security in web applications. You’ll take your first steps in extracting data from a domain using OSINT tools and using Python tools to perform forensics tasks. By the end of this book, you will be able to make the most of Python to test the security of your network and applications.
Table of Contents (23 chapters)
1
Section 1: Python Environment and System Programming Tools
4
Section 2: Network Scripting and Packet Sniffing with Python
8
Section 3: Server Scripting and Port Scanning with Python
12
Section 4: Server Vulnerabilities and Security in Web Applications
16
Section 5: Python Forensics
20
Assessments – Answers to the End-of-Chapter Questions
21
Other Books You May Enjoy
22
Index

Development environments for Python scripting

In this section, we will review PyCharm and Python IDLE as development environments for Python scripting.

Setting up a development environment

In order to rapidly develop and debug Python applications, it is necessary to use an Integrated Development Environment (IDE). If you want to try different options, we recommend you check out the list that is on the official Python site, where you can see the tools according to your operating systems and needs:

https://wiki.python.org/moin/IntegratedDevelopmentEnvironments

Out of all the environments, the following two are the ones we will look at:

Debugging with Python IDLE

Python IDLE is the default IDE that is installed when you install Python in your operating system. Python IDLE allows you to debug your script and see errors and exceptions in the Python shell console:

Figure 1.1: Running a script in the Python shell

In the preceding screenshot, we can see the output in the Python shell and the exception is related to File not found.

PyCharm

PyCharm (https://www.jetbrains.com/pycharm) is a multi-platform tool that we can find for many operating systems, such as Windows, Linux, and macOS X. There are two versions of PyCharm, community and technical, with variations in functionality relating to web framework integration and support for databases. The main advantages of this development environment are as follows:

  • Autocomplete, syntax highlighter, analysis tool, and refactoring
  • Integration with web frameworks, such as Django and Flask
  • An advanced debugger
  • Connection with version control systems, such as Git, CVS, and SVN

In the following screenshot, we can see how to configure virtualenv in PyCharm:

Figure 1.2: Configuring virtualenv in PyCharm

In the preceding screenshot, we are setting the configuration related to establishing a new environment for the project using Virtualenv.

Debugging with PyCharm

In this example, we are debugging a Python script that is applying simple inheritance. An interesting topic is the possibility of adding a breakpoint to our script. In the following screenshot, we are setting a breakpoint in the __init__ method of the class ChildClass:

Figure 1.3: Setting a breakpoint in PyCharm

With the View Breakpoint option, we can see the breakpoint established in the script:

Figure 1.4: Viewing breakpoints in PyCharm

In the following screenshot, we can visualize the values of the parameters that contain the values we are debugging:

Figure 1.5: Debugging variables in PyCharm

In this way, we can know the state of each of the variables at runtime, as well as modify their values to change the logic of our script.