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Data Science for Security and Fraud

This course provides an introduction to tackling digital security and fraud challenges using data science. We will kick off with an overview of problem areas like fake account creation, account takeover, bot traffic, and phishing, and propose a framework for understanding and addressing them. We will then explore common data sources such as web application logs and telemetry collected from devices, networks, and user behavior. Each week, we will apply data science techniques like anomaly detection and graph analysis to tackle a specific problem. At the end of this course, you will be able to build solutions to identify malicious and fraudulent behavior on the internet, and design ways to stop them.

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Yiing Chau Mak
Head of Data at MetaMap
Real-world projects that teach you industry skills.
Learn alongside a small group of your professional peers
Part-time program with 2 live events per week:
Monday @ 4:00 PM UTC
Project Session
Wednesday @ 5:00 PM UTC
Next Cohort
October 3, 2022
4 weeks
US$ 400
or included with membership

Course taught by expert instructors

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Yiing Chau Mak

Head of Data at MetaMap

Mak is currently Head of Data at MetaMap. Previously, he led data science at Shape Security, where he helped Fortune 500 companies detect and stop malicious traffic – by bad bots, bad humans, and everything in between. As Director of Data Science at F5 (which acquired Shape), he built a real-time machine learning-based system that tackled many digital security and fraud challenges, such as fake account creation, account takeover, and unemployment claims. In his past life, Mak worked at the intersection of cybersecurity, cybercrime and personal data protection at the Cyber Security Agency of Singapore, where he led the creation of Singapore’s cybersecurity legislation and strategy.

The course

Learn and apply skills with real-world projects.

You will examine a real (live) web application, and build and deploy a script / system to try to take over accounts on the application. Your actual goal (!) is to try to steal as much money as possible from a bank.
    • How web applications work. In particular, how web content is delivered over the internet, and how to inspect websites and web/API traffic.
    • How to think about web application security. Core security paradigms, what “identity” really means online, how attackers evolve over time, and some thoughts on rules-based vs. AI/ML systems for security
    • How to think like an attacker. Common vulnerabilities and process/security loopholes in web applications, and how to probe for them. Top techniques employed by attackers (hint: it is not all about technology).
    You will analyze logs from a web server/application (which you and your classmates attacked the week before!), and use anomaly detection techniques to identify and profile any suspected automated (bot) logins to the web application.
      • Analyzing web application data. Understand what qualifies as “useful” data for security and fraud, where to obtain such data, and how to process and analyze it.
      • Bots on the internet: a primer. Learn all about bots and why they are necessary to commit fraud at scale. Understand how they work, how to differentiate between bots and humans, and how bots manifest in web application traffic. Also: why captchas are ineffective at stopping bots.
      • How to engineer useful features for bot detection. Explore concepts such as traffic entropy, and use readily available data from log data to develop such features.
      You will develop a model to classify transactions as legitimate human, fraudster, or bot, with features derived from user interaction data, such as mouse movements and keystrokes. You get to work with clean, high-quality labeled data!
        • What kinds of signals are available via the browser
        • How humans interact with websites differently from bots
        • How good (legitimate human) users interact with websites differently from human fraudsters
        You will set up your own graph, use it to ingest some actual transaction data, and use it to identify potentially fraudulent transactions that are related to a number of "seed" fraud indicators.
          • Graphs 101: why graphs, how they work, and other key concepts.
          • How graphs can be applied to solve security and fraud challenges. Possible frameworks and key considerations.
          • How to set up your own graph (we'll use Tigergraph as an example), ingest data, and start analyzing.

          Real-world projects

          Work on projects that bring your learning to life.
          Made to be directly applicable in your work.

          Live access to experts

          Sessions and Q&As with our expert instructors, along with real-world projects.

          Network & community

          Core reviews a study groups. Share experiences and learn alongside a global network of professionals.

          Support & accountability

          We have a system in place to make sure you complete the course, and to help nudge you along the way.

          Course success stories

          Learn together and share experiences with other industry professionals

          I worked with Mak when he led the data science team at Shape Security. I recall when Mak first expressed interest in working for Shape, he was asked to complete a practical exercise to demonstrate his analytical and presentation skills. The exercise required that applicants review a .csv file with tens of thousands of entries, find the anomalies and present/explain them to a non-technical panel. Applicants weren't told how many anomalies there were but there were ten, and previous applicants typically found 5-7. Mak finished well under the three hour time limit and provided the best analysis and presentation we had ever seen. What's more, Mak not only found all ten anomalies, he found an 11th anomaly we didn't even know about. After joining Shape as a data scientist, Mak was quickly promoted to lead the entire team. The net is, I would cancel a family vacation to attend one of Mak's sessions.

          Dan WoodsGlobal Head of Intelligence, F5

          Mak is the real deal. In a space that's flooded with hype and FUD, Mak brings actionable knowledge to the table by focusing his curriculum on the highest impact practical problems in security and risk. The diversity of his professional experience means that you will not only get top-tier technical instruction on the applying data science to fraud problems, but will also get a rare opportunity to combine this with attacker economics and philosophy. This is not a course to miss.

          Clarence ChioCofounder & CTO, Unit21

          This course is for...

          Data scientists and analysts who are curious about the security and fraud space, or who need to defend their organizations and products from online fraud and abuse

          Cybersecurity practitioners and fraud/abuse/trust and safety analysts who want to tackle online security and fraud problems at scale


          • Ability to write Python fluently, and manipulate data within Python.
          • Basic understanding of statistics and probability.
          • Data science and fraud/security experience are not required.

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