Machine Learning for Cybersecurity Analytics
( 25 Modules )

Module #1
Introduction to Cybersecurity Analytics
Overview of cybersecurity analytics, importance of machine learning, and course objectives

Module #2
Fundamentals of Machine Learning
Introduction to machine learning, types of machine learning, and key concepts

Module #3
Mathematical Foundations of Machine Learning
Linear algebra, calculus, probability, and statistics for machine learning

Module #4
Supervised Learning
Introduction to supervised learning, regression, and classification

Module #5
Unsupervised Learning
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #6
Introduction to Cybersecurity Data
Sources, types, and characteristics of cybersecurity data

Module #7
Data Preprocessing for Cybersecurity
Handling missing values, data normalization, and feature engineering

Module #8
Anomaly Detection
Introduction to anomaly detection, types, and algorithms

Module #9
Anomaly Detection with Machine Learning
Applying machine learning algorithms for anomaly detection

Module #10
Network Traffic Analysis
Analysis of network traffic data, protocols, and packet captures

Module #11
Machine Learning for Network Traffic Analysis
Applying machine learning algorithms for network traffic analysis

Module #12
Endpoint Detection and Response
Introduction to endpoint detection and response, and the role of machine learning

Module #13
Malware Analysis with Machine Learning
Applying machine learning algorithms for malware analysis

Module #14
Incident Response with Machine Learning
Applying machine learning algorithms for incident response

Module #15
Deep Learning for Cybersecurity
Introduction to deep learning, and its applications in cybersecurity

Module #16
Adversarial Machine Learning
Introduction to adversarial machine learning, and its implications for cybersecurity

Module #17
Evasion Techniques and Countermeasures
Evasion techniques used by attackers, and countermeasures using machine learning

Module #18
Explainability and Interpretability in Cybersecurity
Explainability and interpretability techniques for machine learning models in cybersecurity

Module #19
Human-Machine Teaming for Cybersecurity
Collaborative approach between humans and machines for cybersecurity analytics

Module #20
Cybersecurity Analytics with Graphs
Graph-based methods for cybersecurity analytics

Module #21
Cybersecurity Analytics with Time-Series Data
Time-series analysis for cybersecurity analytics

Module #22
Case Studies in Cybersecurity Analytics
Real-world case studies of machine learning applications in cybersecurity analytics

Module #23
Ethics and Bias in Cybersecurity Analytics
Ethical considerations and bias in machine learning for cybersecurity analytics

Module #24
Cybersecurity Analytics Toolkits and Frameworks
Overview of popular toolkits and frameworks for cybersecurity analytics

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Cybersecurity Analytics career

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Machine Learning for Cybersecurity Analytics ( 25 Modules )

Machine Learning for Cybersecurity Analytics
( 25 Modules )