Book Image

Hands-On Penetration Testing with Python

By : Furqan Khan
Book Image

Hands-On Penetration Testing with Python

By: Furqan Khan

Overview of this book

With the current technological and infrastructural shift, penetration testing is no longer a process-oriented activity. Modern-day penetration testing demands lots of automation and innovation; the only language that dominates all its peers is Python. Given the huge number of tools written in Python, and its popularity in the penetration testing space, this language has always been the first choice for penetration testers. Hands-On Penetration Testing with Python walks you through advanced Python programming constructs. Once you are familiar with the core concepts, you’ll explore the advanced uses of Python in the domain of penetration testing and optimization. You’ll then move on to understanding how Python, data science, and the cybersecurity ecosystem communicate with one another. In the concluding chapters, you’ll study exploit development, reverse engineering, and cybersecurity use cases that can be automated with Python. By the end of this book, you’ll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits.
Table of Contents (18 chapters)

Machine Learning and Cybersecurity

These days, Machine Learning (ML) is a term we come across quite often. In this chapter, we are going to look at an overview of what exactly ML is, what kinds of problems it solves, and finally what kinds of applications it can have in the cyber security ecosystem. We are also going to look at the various different kinds of ML models, and which models we can use in which circumstances. It should be noted that the scope of this book is not to cover ML in detail, but instead to provide a solid understanding of ML and its applications in the cyber security domain.

The following topics will be covered in this chapter in detail:

  • Machine Learning
  • Regression-based Machine Learning models
  • Classification models
  • Natural language processing