ML-CYBERSEC.AJ1
Hands-On Machine Learning For Cybersecurity
Acquire the skills to harness machine learning (ML) for proactive cybersecurity defense and infrastructure security.
- Practice in 19 Hands-On Labs — nothing to install
- 12 Interactive Lessons and 69 topics mapped to the official exam objectives
- 120 Practice Test Questions
Intermediate Self-paced · 1 year access
19 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
12Interactive Lessons
69Topics
19LiveLab
120Practice Test Questions
56Flashcards
56Glossary of terms
01 / Skills you'll get
What you will be able to do
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.
- 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
Course Highlights
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12 Structured Lessons Comprehensive coverage of core course objectives
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19 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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120 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
12 Interactive Lessons · 69 topics01 Preface 3 topics +
- Who this course is for
- What this course covers
- To get the most out of this course
02 Basics of Machine Learning in Cybersecurity 2 topics +
- What is machine learning?
- Summary
03 Time Series Analysis and Ensemble Modeling 10 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
04 Segregating Legitimate and Lousy URLs 7 topics · 3 LiveLab +
- 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
3 LiveLab in this lesson — see the labs panel →
05 Knocking Down CAPTCHAs 3 topics +
- Characteristics of CAPTCHA
- Using artificial intelligence to crack CAPTCHA
- Summary
06 Using Data Science to Catch Email Fraud and Spam 3 topics · 2 LiveLab +
- Email spoofing
- Spam detection
- Summary
2 LiveLab in this lesson — see the labs panel →
07 Efficient Network Anomaly Detection Using k-means 8 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
08 Decision Tree and Context-Based Malicious Event Detection 14 topics · 5 LiveLab +
- 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
5 LiveLab in this lesson — see the labs panel →
09 Catching Impersonators and Hackers Red Handed 4 topics · 1 LiveLab +
- Understanding impersonation
- Different types of impersonation fraud
- Levenshtein distance
- Summary
1 LiveLab in this lesson — see the labs panel →
10 Changing the Game with TensorFlow 9 topics +
- 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 4 topics · 4 LiveLab +
- Machine learning to detect financial fraud
- Logistic regression classifier – under-sampled data
- Deep learning time
- Summary
4 LiveLab in this lesson — see the labs panel →
12 Case Studies 2 topics · 1 LiveLab +
- Introduction to our password dataset
- Summary
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
19 LiveLabs- Creating a Time Series Model to Predict DDoS Attacks
- Detecting Cyber Attacks Using the Voting Ensemble Method
- Using Heuristics to Detect Malicious Pages
- Comparing Different ML Models to Detect Malicious URLs
- Using a Multiclass Classifier to Detect Malicious URLs
- Using Logistic Regression to Detect Spam SMS
- Creating a Naive Bayes Spam Classifier
- Using k-Means to Detect Anomalies in a Network
- 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
- Using Authorship Attribution for Detecting Real Tweets
- 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
- Creating a Password Tester
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What are the prerequisites for this course? +
While no formal prerequisites are required, a basic understanding of programming concepts, machine learning in threat detection, and Python language is recommended.
What kind of job opportunities can I expect after completing this AI-powered cybersecurity training? +
Potential job roles include cybersecurity analyst, data scientist, machine learning engineer, and security researcher.
How will this course impact my career? +
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.
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