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

Testing for data and model quality

So far, we have seen the technical difficulties that we face in the implementation of our detection systems.

More generally, every time we decide to use algorithms within our cybersecurity solutions, we must take into account the aspects of data quality and model quality, in order to ensure not only the accuracy of predictions, but also their reliability.

Let's continue by analyzing the aspects concerning the data quality process.

Assessing data quality

As we have repeated several times throughout the book, and particularly in Chapter 9, Evaluating Algorithms, the choice of algorithm is undoubtedly important, but the selection of data is even more crucial for the achievement of our objectives...