Book Image

Mastering Machine Learning for Penetration Testing

By : Chiheb Chebbi
Book Image

Mastering Machine Learning for Penetration Testing

By: Chiheb Chebbi

Overview of this book

Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system.
Table of Contents (13 chapters)

Questions

  1. Although machine learning is an interesting concept, there are limited business applications in which it is useful. (True | False)
  2. Machine learning applications are too complex to run in the cloud. (True | False)
  3. For two runs of k-means clustering, is it expected to get the same clustering results? (Yes | No)
  4. Predictive models having target attributes with discrete values can be termed as:

(a) Regression models
(b) Classification models

  1. Which of the following techniques perform operations similar to dropouts in a neural network?

(a) Stacking
(b) Bagging
(c) Boosting

  1. Which architecture of a neural network would be best suited for solving an image recognition problem?

(a) Convolutional neural network
(b) Recurrent neural network
(c) Multi-Layer Perceptron
(d) Perceptron

  1. How does deep learning differ from conventional machine learning?

(a) Deep learning algorithms can handle more data and run with less supervision from data scientists.
(b) Machine learning is simpler, and requires less oversight by data analysts than deep learning does.

(c) There are no real differences between the two; they are the same tool, with different names.

  1. Which of the following is a technique frequently used in machine learning projects?

(a) Classification of data into categories.
(b) Grouping similar objects into clusters.
(c) Identifying relationships between events to predict when one will follow the other.
(d) All of the above.