Hands-On Machine Learning For Cybersecurity

(ML-CYBERSEC.AJ1)/ISBN:978-1-64459-511-4

This course includes
Lessons
TestPrep
Hand-on Lab

Explore the fundamentals of machine learning and understand its application in the cybersecurity landscape and learn how to prepare and optimize data for machine learning, extracting valuable features for effective threat detection. Whether you're a cybersecurity professional, IT enthusiast, or someone looking to enter the field, Hands-On Machine Learning For Cybersecurity course is your gateway to mastering machine learning for cybersecurity.

Lessons

12+ Lessons | 19+ Exercises | 82+ Quizzes | 56+ Flashcards | 56+ Glossary of terms

TestPrep

Hand on lab

19+ LiveLab | 18+ Video tutorials | 41+ Minutes

Here's what you will learn

Download Course Outline

Lessons 1: Preface

  • Who this course is for
  • What this course covers
  • To get the most out of this course

Lessons 2: Basics of Machine Learning in Cybersecurity

  • What is machine learning?
  • Summary

Lessons 3: Time Series Analysis and Ensemble Modeling

  • What is a time series?
  • Classes of time series models
  • Time series decomposition
  • Use cases for time series
  • Time series analysis in cybersecurity
  • Time series trends and seasonal spikes
  • Predicting DDoS attacks
  • Ensemble learning methods
  • Voting ensemble method to detect cyber attacks
  • Summary

Lessons 4: Segregating Legitimate and Lousy URLs

  • Introduction to the types of abnormalities in URLs
  • Using heuristics to detect malicious pages
  • Using machine learning to detect malicious URLs 
  • Logistic regression to detect malicious URLs
  • SVM to detect malicious URLs
  • Multiclass classification for URL classification
  • Summary

Lessons 5: Knocking Down CAPTCHAs

  • Characteristics of CAPTCHA
  • Using artificial intelligence to crack CAPTCHA
  • Summary

Lessons 6: Using Data Science to Catch Email Fraud and Spam

  • Email spoofing 
  • Spam detection
  • Summary

Lessons 7: Efficient Network Anomaly Detection Using k-means

  • Stages of a network attack
  • Dealing with lateral movement in networks
  • Using Windows event logs to detect network anomalies
  • Ingesting active directory data
  • Data parsing
  • Modeling
  • Detecting anomalies in a network with k-means
  • Summary

Lessons 8: Decision Tree and Context-Based Malicious Event Detection

  • Adware
  • Bots
  • Bugs
  • Ransomware
  • Rootkit
  • Spyware
  • Trojan horses
  • Viruses
  • Worms
  • Malicious data injection within databases
  • Malicious injections in wireless sensors
  • Use case
  • Revisiting malicious URL detection with decision trees
  • Summary

Lessons 9: Catching Impersonators and Hackers Red Handed

  • Understanding impersonation
  • Different types of impersonation fraud 
  • Levenshtein distance
  • Summary

Lessons 10: Changing the Game with TensorFlow

  • Introduction to TensorFlow
  • Installation of TensorFlow
  • TensorFlow for Windows users
  • Hello world in TensorFlow
  • Importing the MNIST dataset
  • Computation graphs
  • Tensor processing unit
  • Using TensorFlow for intrusion detection
  • Summary

Lessons 11: Financial Fraud and How Deep Learning Can Mitigate It

  • Machine learning to detect financial fraud
  • Logistic regression classifier – under-sampled data
  • Deep learning time
  • Summary

Lessons 12: Case Studies

  • Introduction to our password dataset
  • Summary

Hands-on LAB Activities

Time Series Analysis and Ensemble Modeling

  • Creating a Time Series Model to Predict DDoS Attacks
  • Detecting Cyber Attacks Using the Voting Ensemble Method

Segregating Legitimate and Lousy URLs

  • Using Heuristics to Detect Malicious Pages
  • Comparing Different ML Models to Detect Malicious URLs
  • Using a Multiclass Classifier to Detect Malicious URLs

Using Data Science to Catch Email Fraud and Spam

  • Using Logistic Regression to Detect Spam SMS
  • Creating a Naive Bayes Spam Classifier

Efficient Network Anomaly Detection Using k-means

  • Using k-Means to Detect Anomalies in a Network

Decision Tree and Context-Based Malicious Event Detection

  • Using Decision Trees and Random Forests for Classifying Malicious Data
  • Detecting Rootkits
  • Exploiting a Website Using SQL Injection
  • Detecting Anomaly Using Isolation Forest
  • Detecting Malicious URL With Decision Trees

Catching Impersonators and Hackers Red Handed

  • Using Authorship Attribution for Detecting Real Tweets

Financial Fraud and How Deep Learning Can Mitigate It

  • Detecting Credit Card Fraud
  • Building a Logistic Regression Classifier for Under-Sampled Data
  • Building a Logistic Regression Classifier for Skewed Data
  • Building a Deep Learning Classifier for Under-Sampled Data

Case Studies

  • Creating a Password Tester