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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

Detecting metamorphic malware with HMMs

The examples of algorithms applied to malware detection that have been shown so far were intended to automate some of the routine activities performed by malware analysts.

However, the analysis methodology on which they are based is essentially static malware analysis.

Many of the concrete cases of malware threats, however, are not easily identifiable with this method of analysis, as the malware developers have learned how to work around the detection techniques based on signatures.

It will therefore be necessary to adopt a different methodology to identify the malicious behavior of more advanced malware, and to this end, we will have to move to an approach based on dynamic malware analysis, combining it with the appropriate algorithms.

But to adequately address the problem, it is necessary to understand in detail the limits of traditional...

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