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Hands-On Artificial Intelligence for Cybersecurity

You're reading from   Hands-On Artificial Intelligence for Cybersecurity Implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789804027
Length 342 pages
Edition 1st Edition
Languages
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Author (1):
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Alessandro Parisi Alessandro Parisi
Author Profile Icon Alessandro Parisi
Alessandro Parisi
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Table of Contents (16) Chapters Close

Preface 1. Section 1: AI Core Concepts and Tools of the Trade FREE CHAPTER
2. Introduction to AI for Cybersecurity Professionals 3. Setting Up Your AI for Cybersecurity Arsenal 4. Section 2: Detecting Cybersecurity Threats with AI
5. Ham or Spam? Detecting Email Cybersecurity Threats with AI 6. Malware Threat Detection 7. Network Anomaly Detection with AI 8. Section 3: Protecting Sensitive Information and Assets
9. Securing User Authentication 10. Fraud Prevention with Cloud AI Solutions 11. GANs - Attacks and Defenses 12. Section 4: Evaluating and Testing Your AI Arsenal
13. Evaluating Algorithms 14. Assessing your AI Arsenal 15. Other Books You May Enjoy

Network Anomaly Detection with AI

The current level of interconnection that can be established between different devices (for example, think of the Internet of Things (IoT)) has reached such a complexity that it seriously questions the effectiveness of traditional concepts such as perimeter security. As a matter of fact, cyberspace's attack surface grows exponentially, and it is therefore essential to resort to automated tools for the effective detection of network anomalies associated with unprecedented cybersecurity threats.

This chapter will cover the following topics:

  • Network anomaly detection techniques
  • How to classify network attacks
  • Detecting botnet topology
  • Different machine learning (ML) algorithms for botnet detection

In this chapter, we will focus on anomaly detection related to network security, postponing the discussion of the aspects of fraud detection...

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