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

Telling different malware families apart

We have seen the advantages and limitations associated with traditional malware analysis methodologies, and we have understood why—in light of the high prevalence of malware threats—it is necessary to introduce algorithmic automation methods for malware detection.

In particular, it is increasingly important that the similarities in malware behavior are correctly identified, which means that malware samples must be associated to classes or families of the same type, even if the individual malware signatures are not comparable to each other, due to, for example, the presence of polymorphic codes that alter the hash checksums accordingly.

The analysis of similarities can be carried out in an automated form, by using clustering algorithms.

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