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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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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: AI Core Concepts and Tools of the Trade
2. Introduction to AI for Cybersecurity Professionals FREE CHAPTER 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

Malware Threat Detection

The high diffusion of malware and ransomware codes, together with the rapid polymorphic mutation in the different variants (polymorphic and metamorphic malware) of the same threats, has made traditional detection solutions based on signatures and hashing of image files obsolete, on which most common antivirus software is based.

It is therefore increasingly necessary to resort to machine learning (ML) solutions that allow a rapid screening (triage) of threats, focusing attention on not wasting scarce resources such as a malware analyst's skills and efforts.

This chapter will cover the following topics:

  • Introducing the malware analysis methodology
  • How to tell different malware families apart
  • Decision tree malware detectors
  • Detecting metamorphic malware with Hidden Markov Models (HMMs)
  • Advanced malware detection with deep learning
...
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