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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

NLP to the rescue

One of the most exciting areas of AI is certainly NLP, which consists of the analysis and automated understanding of human language.

The purpose of NLP is to try to extract sensible information from unstructured data (such as email messages, tweets, and Facebook posts).

The fields of application of NLP are huge, and vary from simultaneous translations to sentiment analysis speech recognition.

NLP steps

The phases that characterize NPL are as follows:

  1. Identification of the words (tokens) constituting the language
  2. Analysis of the structure of the text
  3. Identification of the relationships between words (in paragraphs, sentences, and so on)
  4. Semantic analysis of the text

One of the best known Python libraries...

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