Introduction to Machine Learning in Cybersecurity

Why a security analyst benefits from thinking statistically, where ML already runs in production security tools, the four threat families, the Cyber Kill Chain, a short history of learning-based defence, a worked blacklist-versus-classifier example, and the five-stage ML pipeline every later unit reuses.

Learning outcomes

Explain why a learned classifier generalises to new threats where a fixed rule list cannot
Map each of the four threat families and the Cyber Kill Chain's seven stages to the family of ML that defends it best
Build and evaluate a Naive Bayes spam classifier with scikit-learn and read a classification report
Walk through the five-stage ML pipeline (collect, engineer features, train, evaluate, deploy and monitor) for a security problem

Topics

1.1

Why a Security Analyst Needs to Think Like a Statistician

The locksmith analogy for signature matching versus learned detection, and the three families of machine learning, supervised, unsupervised and reinforcement, each matched to a unit later in this course.

5 min

1.2

Where Machine Learning Already Lives in Cybersecurity

A tour of the production security tools already running a trained model quietly in the background, from email spam scoring to endpoint protection to fraud monitoring, mapped to the unit of this course that covers each one.

5 min

Assessments

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Unit Assessment

Introduction to Machine Learning in Cybersecurity

20 questions30 minPass: 60%

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Comprehensive Assessment

1.3

The Cybersecurity Threat Landscape, in Four Families

Malware, social engineering, network intrusion, and fraud and abuse, the four broad threat families this course is organised around, and the 2002 Paul Graham spam classifier case study that shows why a learned model outlasts a hand-written rule list.

5 min

1.4

The Cyber Kill Chain: Where Machine Learning Fits at Each Stage

Lockheed Martin's seven-stage Cyber Kill Chain, and which family of trained model helps a defender break it at each stage, from reconnaissance through actions on objectives.

5 min

1.5

A Short History of Learning-Based Defence

A real timeline from Paul Graham's 2002 statistical spam filter to adversarial attacks against deployed classifiers, showing that offensive and defensive machine learning milestones have always pushed each other forward.

5 min

1.6

A First Example: Blacklist versus Learned Classifier

A worked, runnable comparison of a keyword blacklist filter against a Naive Bayes spam classifier trained with scikit-learn, including a real classification report and the real class-imbalance chart from the course's own spam corpus.

5 min

1.7

The ML Pipeline for Security Analytics

The five-stage pipeline, collect, engineer features, train, evaluate, deploy and monitor, that every later unit and example in this course builds a piece of, plus why a disappointing model is more often a feature problem than an algorithm problem.

5 min

1.8

Unit 1 Quick Reference, Self-Study and Lab

A one-page summary table of every topic in this unit, six self-study questions to test understanding, and a hands-on exercise counting real class imbalance in your own inbox.

5 min

ML for Cyber Security: Complete Assessment

100 questions120 minPass: 60%

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