Book Image

Hands-On Artificial Intelligence for Cybersecurity

By : Parisi
Book Image

Hands-On Artificial Intelligence for Cybersecurity

By: 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

Predictive analytics for credit card fraud detection

To adequately address the problem of fraud detection, it is necessary to develop predictive analytics models, that is, mathematical models that can identify trends within the data, using a data-driven approach.

Unlike descriptive analytics (whose paradigm is constituted by business intelligence (BI)), which limits itself to classifying the past data on the basis of measures deriving from the application of descriptive statistics (such as sums, averages, variances, and so on), precisely describe the characteristics of the data being analyzed; instead, by looking at the present and past situation, predictive analytics tries to project itself in order to predict future events with a certain degree of probability. It does this by extrapolating hidden patterns within the analyzed data.

Being data-driven, predictive analytics makes...