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Hands-On Artificial Intelligence for Cybersecurity

Hands-On Artificial Intelligence for Cybersecurity

By : Parisi
4.4 (5)
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Hands-On Artificial Intelligence for Cybersecurity

Hands-On Artificial Intelligence for Cybersecurity

4.4 (5)
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)
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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

Evolution in AI: from expert systems to data mining

To understand the advantages associated with the adoption of AI in the field of cybersecurity, it is necessary to introduce the underlying logic to the different methodological approaches that characterize AI.

We will start with a brief historical analysis of the evolution of AI in order to fully evaluate the potential benefits of applying it in the field of cybersecurity.

A brief introduction to expert systems

One of the first attempts at automated learning consisted of defining the rule-based decision system applied to a given application domain, covering all the possible ramifications and concrete cases that could be found in the real world. In this way, all the possible...

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