Artificial Intelligence for Cybersecurity

(AI-CYBSEC.AJ1) / ISBN : 978-1-64459-483-4
This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)
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About This Course

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.

Skills You’ll Get

Get the support you need. Enroll in our Instructor-Led Course.

Lessons

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

TestPrep

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

Hands-On Labs

27+ LiveLab | 00+ Minutes

1

Preface

  • Who this course is for
  • What this course covers
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
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
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
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
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
7

Securing User Authentication

  • Authentication abuse prevention
  • Account reputation scoring
  • User authentication with keystroke recognition
  • Biometric authentication with facial recognition
  • Summary
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
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
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
11

Assessing your AI Arsenal

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

1

Introduction to AI for Cybersecurity Professionals

  • Creating a Linear Regression Model
  • Creating a Clustering Model
  • Using Neural Networks for Spam Filtering
2

Setting Up Your AI for Cybersecurity Arsenal

  • Performing Matrix Operations
  • Using a Linear Regression Model for Prediction
3

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
4

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
5

Network Anomaly Detection with AI

  • Detecting Botnet
  • Performing Gaussian Anomaly Detection
6

Securing User Authentication

  • Detecting Anomaly Using Keystrokes
  • Creating an Image Classification Model
  • Understanding Covariance Matrix
7

Fraud Prevention with Cloud AI Solutions

  • Performing Oversampling and Undersampling
  • Comparing Different Models for Detecting Credit Card Frauds
9

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
10

Assessing your AI Arsenal

  • Handling Missing Values in a Dataset
  • Performing Hyperparameter Optimization

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