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Data Science for Security and Fraud - Course Homepage

Table of contents
  1. Course Notes and Projects
    1. Week 1
      1. Notes
      2. Project
    2. Week 2
      1. Notes
  2. Lecture Slides

This page contains an outline of the entire course, as well as links to all course materials.

Course Notes and Projects

Week 1

Notes

  • Thinking Like an Attacker — How do web attackers exploit applications for financial gain through credential resale, money laundering, headless browser automation, and MFA-bypassing phishing proxies, and why does cyber fraud remain low-risk yet highly profitable? 📕This article forms part of the notes from Week 1 of the Data Science for Security and Fraud online

  • Web Applications 101 — How do web applications work? What are the key concepts that you should know, and some tools for automating web requests? 📕This article forms part of the notes from Week 1 of the Data Science for Security and Fraud online course. Access the full course outline here. We’ve grown so

  • Using Browser Developer Tools and Postman — Learn how to use browser dev tools and Postman to analyze web traffic. 📕This article forms part of the notes from Week 1 of the Data Science for Security and Fraud online course. Access the full course outline here. In this video, you will learn how to use Developer Tools

Project

  • Week 1 Project: Attacking Alpha Bank — 🧰This article is the project for Week 1 of the Data Science for Security and Fraud online course. Access the full course outline here. You are trying to join The Shadows, a shadowy international gang that is rumored to be the world’s most profitable crime syndicate. Its members and methods

Week 2

Notes

  • Analyzing web application data — Learn how to effectively analyze and clean web application data, ensure consistency across logs, enrich the dataset with additional information, explore traffic patterns, identify anomalies, and build accurate models for fraud detection. 📕This article forms part of the notes from Week 2 of the Data Science for Security and Fraud

  • Detecting bot traffic — How can we differentiate bot from human traffic? What are some typical features? 📕This article forms part of the notes from Week 2 of the Data Science for Security and Fraud online course. Access the full course outline here. How to identify (bad) bot traffic? It is always possible to

Lecture Slides

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Week 1 Lecture

Week 1 Lecture.pdf

4 MB

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](/images/dssf-home/Week-1-Lecture.pdf “Download”)