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 the quality of our predictions

To correctly evaluate the quality of the predictions that were obtained by our classifiers, we cannot be satisfied with just accuracy_score, but must also use other measures, such as the F1 score and the ROC curve, which we previously encountered in Chapter 5, Network Anomalies Detection with AI, dealing with the topic related to anomaly detection.

F1 value

For the convenience, let's briefly go over the metrics that were previously introduced and their definitions:

Sensitivity or True Positive Rate (TPR) = True Positive / (True Positive + False Negative);

Here, sensitivity is also known as the recall rate:

False Positive Rate (FPR) = False Positive / (False Positive + True Negative...