Search this site
Embedded Files
Skip to main content
Skip to navigation
ML for Cybersecurity
Home
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
Post-Add-on-Lab
1. How to improve the performance on the
malicious website detection
by re-sampling the dataset？
2. Try to use other algorithms
on
this
webpage classifier
and compare the result.
3. Try to use
a Random Forest classifier
to train this dataset.
Google Sites
Report abuse
Page details
Page updated
Google Sites
Report abuse