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ML for Cybersecurity
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M0. Getting Started with CoLab on ML for CyberSecurity
Overview
Helloworld example
M1. Naive Bayes for spam email filtering
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M2. Logistic Regression for financial fraud prediction
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M3. Neural network algorithms for network DOS detection
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M4. Convolutional Neural Network (CNN) for CAPTCHA Bypass
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M5. Decision Tree for website phishing
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M6. Deep learning for malware classification and protection
Pre-Lab
Hands-on Lab Practice with Deep Neural Network
Post-Add-on-Lab
M7. Support Vector Machine for anomaly-based intrusion detection
Pre-Lab
Hands-on Lab Practice using Support Vector Machine
Post-Add-on-Lab
M8. K-Means clustering for Detecting Ransomware
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M9. Decision Tree for malicious web application detections
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
M10. KNN Classification for user behavior anomaly detection
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
Webinar Spring 2024
Faculty workshop 2023
Faculty Workshop 2024
Faculty Workshop 2025
ML for Cybersecurity
M9.
Decision Tree for malicious web application detection (malicious pages, URLs, HTTP requests)
Pre-Lab
Hands-on Lab Practice
Post-Add-on-Lab
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