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
Section 1: AI Core Concepts and Tools of the Trade
Section 2: Detecting Cybersecurity Threats with AI
Section 3: Protecting Sensitive Information and Assets
Section 4: Evaluating and Testing Your AI Arsenal

Evaluating a detector's performance with ROC

We have previously encountered the ROC curve and AUC measure (Chapter 5, Network Anomaly Detection with AI, and Chapter 7, Fraud Prevention with Cloud AI Solutions) to evaluate and compare the performance of different classifiers.

Now let's explore the topic in a more systematic way, introducing the confusion matrix associated with all the possible results returned by a fraud-detection classifier, comparing the predicted values with the real values:

We can then calculate the following values (listed with their interpretation) based on the previous confusion matrix:

  • Sensitivity = Recall = Hit rate = TP/(TP + FP): This value measures the rate of correctly labeled fraudsters and represents the true positive rate (TPR)
  • False Positive Rate (FPR) = FP/(FP + TN): FPR is also calculated as 1 – Specificity
  • Classification accuracy...