Book Image

Mastering Kali Linux for Advanced Penetration Testing – Fourth Edition - Fourth Edition

By : Vijay Kumar Velu
Book Image

Mastering Kali Linux for Advanced Penetration Testing – Fourth Edition - Fourth Edition

By: Vijay Kumar Velu

Overview of this book

Remote working has given hackers plenty of opportunities as more confidential information is shared over the internet than ever before. In this new edition of Mastering Kali Linux for Advanced Penetration Testing, you’ll learn an offensive approach to enhance your penetration testing skills by testing the sophisticated tactics employed by real hackers. You’ll go through laboratory integration to cloud services so that you learn another dimension of exploitation that is typically forgotten during a penetration test. You'll explore different ways of installing and running Kali Linux in a VM and containerized environment and deploying vulnerable cloud services on AWS using containers, exploiting misconfigured S3 buckets to gain access to EC2 instances. This book delves into passive and active reconnaissance, from obtaining user information to large-scale port scanning. Building on this, different vulnerability assessments are explored, including threat modeling. See how hackers use lateral movement, privilege escalation, and command and control (C2) on compromised systems. By the end of this book, you’ll have explored many advanced pentesting approaches and hacking techniques employed on networks, IoT, embedded peripheral devices, and radio frequencies.
Table of Contents (17 chapters)
15
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16
Index

Using machine learning for reconnaissance

Machine learning has become a vital technology in cybersecurity. It is the art of using data and algorithms to imitate the way we learn as humans. Machine learning is a branch of artificial intelligence. In this section, we will explore the GyoiThon tool, which you can leverage during large-scale pentesting or red team activities.

There are four types of machine learning algorithms:

  • Supervised: These learning algorithms are provided with a set of known data (labeled) that includes the desired output. The goal of this type of learning is for the algorithm to achieve a high level of accuracy by learning from patterns in the data to make predictions.
  • Unsupervised: These learning algorithms are trained with unlabeled data or datasets that do not include the desired output. The algorithm tries to interpret and organize the datasets.
  • Semi-supervised: This is a mix of the preceding types.
  • Reinforcement learning: These...