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Hands-On Artificial Intelligence for Cybersecurity

By : Parisi

4.4 (5)

Buy this Book

Hands-On Artificial Intelligence for Cybersecurity

4.4 (5)

By: Parisi

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Section 1: AI Core Concepts and Tools of the Trade

Introduction to AI for Cybersecurity Professionals

Applying AI in cybersecurity

Evolution in AI: from expert systems to data mining

Types of machine learning

Algorithm training and optimization

Getting to know Python's libraries

AI in the context of cybersecurity

Summary

Setting Up Your AI for Cybersecurity Arsenal

Getting to know Python for AI and cybersecurity

Python libraries for cybersecurity

Enter Anaconda – the data scientist's environment of choice

Playing with Jupyter Notebooks

Installing DL libraries

Summary

Section 2: Detecting Cybersecurity Threats with AI

Ham or Spam? Detecting Email Cybersecurity Threats with AI

Detecting spam with Perceptrons

Spam detection with SVMs

Phishing detection with logistic regression and decision trees

Spam detection with Naive Bayes

NLP to the rescue

Summary

Malware Threat Detection

Malware analysis at a glance

Telling different malware families apart

Decision tree malware detectors

Detecting metamorphic malware with HMMs

Advanced malware detection with deep learning

Summary

Network Anomaly Detection with AI

Network anomaly detection techniques

How to classify network attacks

Detecting botnet topology

Different ML algorithms for botnet detection

Summary

Section 3: Protecting Sensitive Information and Assets

Securing User Authentication

Authentication abuse prevention

Account reputation scoring

User authentication with keystroke recognition

Biometric authentication with facial recognition

Summary

Fraud Prevention with Cloud AI Solutions

Introducing fraud detection algorithms

Predictive analytics for credit card fraud detection

Getting to know IBM Watson Cloud solutions

Importing sample data and running Jupyter Notebook in the cloud

Evaluating the quality of our predictions

Summary

GANs - Attacks and Defenses

GANs in a nutshell

GAN Python tools and libraries

Network attack via model substitution

IDS evasion via GAN

Facial recognition attacks with GAN

Summary

Section 4: Evaluating and Testing Your AI Arsenal

Evaluating Algorithms

Best practices of feature engineering

Evaluating a detector's performance with ROC

How to split data into training and test sets

Using cross validation for algorithms

Summary

Assessing your AI Arsenal

Evading ML detectors

Challenging ML anomaly detection

Testing for data and model quality

Ensuring security and reliability

Summary

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Predictive analytics for credit card fraud detection

To adequately address the problem of fraud detection, it is necessary to develop predictive analytics models, that is, mathematical models that can identify trends within the data, using a data-driven approach.

Unlike descriptive analytics (whose paradigm is constituted by business intelligence (BI)), which limits itself to classifying the past data on the basis of measures deriving from the application of descriptive statistics (such as sums, averages, variances, and so on), precisely describe the characteristics of the data being analyzed; instead, by looking at the present and past situation, predictive analytics tries to project itself in order to predict future events with a certain degree of probability. It does this by extrapolating hidden patterns within the analyzed data.

Being data-driven, predictive analytics makes...