Artificial Intelligence for Cybersecurity

(AI-CYBSEC.AJ1)/ISBN:978-1-64459-483-4

This course includes
Lessons
TestPrep
Hand-on Lab
AI Tutor (Add-on)

In today's rapidly evolving digital landscape, the intersection of artificial intelligence (AI) and cybersecurity is crucial for safeguarding organizations against ever-growing cyber threats. This course is designed to equip you with the knowledge and skills needed to leverage AI techniques for enhancing cybersecurity measures. This course will help you understand the fundamentals of artificial intelligence and its applications in cybersecurity.

Lessons

11+ Lessons | 155+ Exercises | 105+ Quizzes | 63+ Flashcards | 63+ Glossary of terms

TestPrep

50+ Pre Assessment Questions | 50+ Post Assessment Questions |

Hand on lab

27+ LiveLab | 27+ Video tutorials | 56+ Minutes

Here's what you will learn

Download Course Outline

Lessons 1: Preface

  • Who this course is for
  • What this course covers

Lessons 2: 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

Lessons 3: 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

Lessons 4: 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

Lessons 5: 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

Lessons 6: Network Anomaly Detection with AI

  • Network anomaly detection techniques
  • How to classify network attacks
  • Detecting botnet topology
  • Different ML algorithms for botnet detection
  • Summary

Lessons 7: Securing User Authentication

  • Authentication abuse prevention
  • Account reputation scoring
  • User authentication with keystroke recognition
  • Biometric authentication with facial recognition
  • Summary

Lessons 8: 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

Lessons 9: 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

Lessons 10: 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

Lessons 11: Assessing your AI Arsenal

  • Evading ML detectors
  • Challenging ML anomaly detection
  • Testing for data and model quality
  • Ensuring security and reliability
  • Summary

Hands-on LAB Activities

Introduction to AI for Cybersecurity Professionals

  • Creating a Linear Regression Model
  • Creating a Clustering Model
  • Using Neural Networks for Spam Filtering

Setting Up Your AI for Cybersecurity Arsenal

  • Performing Matrix Operations
  • Using a Linear Regression Model for Prediction

Ham or Spam? Detecting Email Cybersecurity Threats with AI

  • Creating a Perceptron-based Spam Filter
  • Creating an SVM Spam Filter
  • Creating a Phishing Detector with Logistic Regression
  • Creating a Phishing Detector with Decision Trees
  • Creating a Spam Detector with NLTK

Malware Threat Detection

  • Using the k-Means Clustering Algorithm for Malware Detection
  • Creating a Decision Tree and a Random Forest Malware Classifier
  • Detecting Malware using an HMM Model

Network Anomaly Detection with AI

  • Detecting Botnet
  • Performing Gaussian Anomaly Detection

Securing User Authentication

  • Detecting Anomaly Using Keystrokes
  • Creating an Image Classification Model
  • Understanding Covariance Matrix

Fraud Prevention with Cloud AI Solutions

  • Performing Oversampling and Undersampling
  • Comparing Different Models for Detecting Credit Card Frauds

Evaluating Algorithms

  • Performing Feature Normalization
  • Dealing with Categorical Data
  • Using Different Measures to Evaluate Algorithms
  • Creating a Learning Curve to Measure Performance of an Algorithm
  • Performing K-Folds Cross Validation

Assessing your AI Arsenal

  • Handling Missing Values in a Dataset
  • Performing Hyperparameter Optimization