Book Image

Hands-On Artificial Intelligence for Cybersecurity

By : Alessandro Parisi
Book Image

Hands-On Artificial Intelligence for Cybersecurity

By: Alessandro Parisi

Overview of this book

Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: AI Core Concepts and Tools of the Trade
4
Section 2: Detecting Cybersecurity Threats with AI
8
Section 3: Protecting Sensitive Information and Assets
12
Section 4: Evaluating and Testing Your AI Arsenal

Evaluating Algorithms

As we have seen in the previous chapters, several AI solutions are available to achieve certain cybersecurity goals, so it is important to learn how to evaluate the effectiveness of various alternative solutions, using appropriate analysis metrics. At the same time, it is important to prevent phenomena such as overfitting, which can compromise the reliability of forecasts when switching from training data to test data.

In this chapter, we will learn about the following topics:

  • Feature engineering best practices in dealing with raw data
  • How to evaluate a detector's performance using the ROC curve
  • How to appropriately split sample data into training and test sets
  • How to manage algorithms' overfitting and bias–variance trade-offs with cross validation

Now, let's begin our discussion of we need feature engineering by examining the very...