Book Image

Hands-On Artificial Intelligence for Cybersecurity

By : Alessandro Parisi
Book Image

Hands-On Artificial Intelligence for Cybersecurity

By: Alessandro 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.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: AI Core Concepts and Tools of the Trade
Section 1: AI Core Concepts and Tools of the Trade
2
Introduction to AI for Cybersecurity Professionals
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
3
Setting Up Your AI for Cybersecurity Arsenal
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
4
Section 2: Detecting Cybersecurity Threats with AI
Section 2: Detecting Cybersecurity Threats with AI
5
Ham or Spam? Detecting Email 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
6
Malware Threat Detection
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
7
Network Anomaly Detection with AI
Network Anomaly Detection with AI
Network anomaly detection techniques
How to classify network attacks
Detecting botnet topology
Different ML algorithms for botnet detection
Summary
8
Section 3: Protecting Sensitive Information and Assets
Section 3: Protecting Sensitive Information and Assets
9
Securing User Authentication
Securing User Authentication
Authentication abuse prevention
Account reputation scoring
User authentication with keystroke recognition
Biometric authentication with facial recognition
Summary
10
Fraud Prevention with Cloud AI Solutions
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
11
GANs - Attacks and Defenses
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
12
Section 4: Evaluating and Testing Your AI Arsenal
Section 4: Evaluating and Testing Your AI Arsenal
13
Evaluating Algorithms
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
14
Assessing your AI Arsenal
Assessing your AI Arsenal
Evading ML detectors
Challenging ML anomaly detection
Testing for data and model quality
Ensuring security and reliability
Summary
15
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Evaluating a detector's performance with ROC

We have previously encountered the ROC curve and AUC measure (Chapter 5, Network Anomaly Detection with AI, and Chapter 7, Fraud Prevention with Cloud AI Solutions) to evaluate and compare the performance of different classifiers.

Now let's explore the topic in a more systematic way, introducing the confusion matrix associated with all the possible results returned by a fraud-detection classifier, comparing the predicted values with the real values:

We can then calculate the following values (listed with their interpretation) based on the previous confusion matrix:

  • Sensitivity = Recall = Hit rate = TP/(TP + FP): This value measures the rate of correctly labeled fraudsters and represents the true positive rate (TPR)
  • False Positive Rate (FPR) = FP/(FP + TN): FPR is also calculated as 1 – Specificity
  • Classification accuracy...
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