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Hands-On Machine Learning For Cybersecurity

Acquire the skills to harness machine learning (ML) for proactive cybersecurity defense and infrastructure security. 

(ML-CYBERSEC.AJ1) / ISBN : 978-1-64459-511-4
This course includes
Interactive Lessons
Gamified TestPrep
Hands-On Labs
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About This Course

This machine learning in cybersecurity course is perfect for you if you want to learn how to use AI to protect systems from hackers. We’ll cover everything from the basics of ML and AI to advanced techniques like time series analysis and ensemble modeling. You’ll also get hands-on experience with tools like TensorFlow and learn how to detect things like network anomalies, malicious URLs, and even financial fraud.

Skills You’ll Get

  • Learn cybersecurity principles, threats, vulnerabilities, and defense mechanisms 
  • Analyze and visualize data to extract insights 
  • Write a Python code for data science and machine learning 
  • Apply time series models for predicting cyber attacks and detecting anomalies 
  • Combine multiple machine learning models for improved performance 
  • Identify unusual patterns in data to detect potential threats 
  • Use NLP techniques for tasks like spam filtering and phishing detection 
  • Apply deep neural networks for complex tasks like image classification and fraud detection 
  • Utilize TensorFlow, a popular deep learning framework 
  • Build and deploy ML models for real-world cybersecurity challenges

Download Course Outline

1

Preface

  • Who this course is for
  • What this course covers
  • To get the most out of this course
2

Basics of Machine Learning in Cybersecurity

  • What is machine learning?
  • Summary
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
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
5

Knocking Down CAPTCHAs

  • Characteristics of CAPTCHA
  • Using artificial intelligence to crack CAPTCHA
  • Summary
6

Using Data Science to Catch Email Fraud and Spam

  • Email spoofing 
  • Spam detection
  • Summary
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
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
9

Catching Impersonators and Hackers Red Handed

  • Understanding impersonation
  • Different types of impersonation fraud 
  • Levenshtein distance
  • Summary
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
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
12

Case Studies

  • Introduction to our password dataset
  • Summary

1

Time Series Analysis and Ensemble Modeling

  • Creating a Time Series Model to Predict DDoS Attacks
  • Detecting Cyber Attacks Using the Voting Ensemble Method
2

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
3

Using Data Science to Catch Email Fraud and Spam

  • Using Logistic Regression to Detect Spam SMS
  • Creating a Naive Bayes Spam Classifier
4

Efficient Network Anomaly Detection Using k-means

  • Using k-Means to Detect Anomalies in a Network
5

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
6

Catching Impersonators and Hackers Red Handed

  • Using Authorship Attribution for Detecting Real Tweets
7

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
8

Case Studies

  • Creating a Password Tester

Any questions?
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Have questions about our AI and machine learning for cybersecurity course? Find answers here.

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While no formal prerequisites are required, a basic understanding of programming concepts, machine learning in threat detection, and Python language is recommended.

Potential job roles include cybersecurity analyst, data scientist, machine learning engineer, and security researcher.

Our practical machine learning for cybersecurity course will equip you with the skills and knowledge needed to pursue a career in cybersecurity, data science, or machine learning.

Learn Practical ML for Cybersecurity

  Develop the expertise to use machine learning and AI for advanced threat detection and prevention.

$ 433.42

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