Book Image

Mastering Python for Networking and Security - Second Edition

By : José Ortega
Book Image

Mastering Python for Networking and Security - Second Edition

By: José Ortega

Overview of this book

It’s now more apparent than ever that security is a critical aspect of IT infrastructure, and that devastating data breaches can occur from simple network line hacks. As shown in this book, combining the latest version of Python with an increased focus on network security can help you to level up your defenses against cyber attacks and cyber threats. Python is being used for increasingly advanced tasks, with the latest update introducing new libraries and packages featured in the Python 3.7.4 recommended version. Moreover, most scripts are compatible with the latest versions of Python and can also be executed in a virtual environment. This book will guide you through using these updated packages to build a secure network with the help of Python scripting. You’ll cover a range of topics, from building a network to the procedures you need to follow to secure it. Starting by exploring different packages and libraries, you’ll learn about various ways to build a network and connect with the Tor network through Python scripting. You will also learn how to assess a network's vulnerabilities using Python security scripting. Later, you’ll learn how to achieve endpoint protection by leveraging Python packages, along with writing forensic scripts. By the end of this Python book, you’ll be able to use Python to build secure apps using cryptography and steganography techniques.
Table of Contents (22 chapters)
1
Section 1: The Python Environment and System Programming Tools
4
Section 2: Network Scripting and Extracting Information from the Tor Network with Python
8
Section 3: Server Scripting and Port Scanning with Python
12
Section 4: Server Vulnerabilities and Security in Python Modules
16
Section 5: Python Forensics

Static code analysis for detecting vulnerabilities

In this section, we will cover Bandit as a static code analyzer for detecting vulnerabilities. We'll do this by reviewing tools we can find in the Python ecosystem for static code analysis and then learning with the help of more detailed tools such as Bandit.

Introducing static code analysis

The objective of static analysis is to search the code and identify potential problems. This is an effective way to find code problems cheaply, compared to dynamic analysis, which involves code execution. However, running an effective static analysis requires overcoming a number of challenges.

For example, if we want to detect inputs that are not being validated when we are using the eval() function or the subprocess module, we could create our own parser that would detect specific rules to make sure that the different modules are used in a secure way.

The simplest form of static analysis would be to search through the code line...