Professional Education Applied Science

Machine Learning for Cybersecurity

Evolve your cyber risk strategy with advanced training in machine learning for cybersecurity from the Center for Data and Computing at the University of Chicago.

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At a Glance

Type
Professional Education
Enrollment
Open Enrollment
Duration
Five weeks Open Enrollment
Format
Remote
Cost
$3,000 for certificate

Many organizations now use machine learning in their operations but have not yet applied these cutting-edge approaches within traditional cybersecurity practices. Researchers at the Center for Data and Computing (CDAC) at the University of Chicago develop and study data-driven methods for applied cybersecurity, including machine learning defenses against data breaches, fraud, and other threats.

From identifying backdoors in neural networks to automatically detecting malware, stolen accounts, or network attacks, machine learning offers essential new protections for businesses and individuals.

In this four-day certificate course, you will develop the technical skills necessary to learn how to deploy data-driven prevention strategies using machine learning and other innovative solutions. Faculty will teach cutting-edge cybersecurity methods using real-world case studies and datasets, building both fundamental and practical knowledge. Information security managers, engineers, and professionals whose role includes working in applied computer security or cybersecurity are encouraged to enroll. Prior experience with machine learning is not required.

Designed For

This certificate is designed for a variety of professionals working across cybersecurity, applied science security, and other roles such as Information Security Managers, DevOps Engineers, Software Developers, and System Administrators

Learning Objectives

Our program is uniquely positioned to strengthen your organization’s cybersecurity measures through an advanced understanding of state-of-the-art machine learning applications. By the end of this program, learners will be able to:

  • Understand basic concepts for statistical modeling, including principles for model selection for supervised and unsupervised learning tasks in the context of cybersecurity.
  • Select the most appropriate models for various cybersecurity scenarios, such as malware classification, botnet detection, and intrusion detection.
  • Detect and defend against adversarial attacks on machine learning models in cybersecurity settings at both training and test times
  • Identify and understand means of navigating legal and ethical challenges that emerge from gathering data about human subjects and using it to build machine-learning models

Course Format

This certificate is offered remotely with live online classes. Your program experience will include:

  • Live Class Sessions: The primary focus of the live-online class sessions will be to discuss real-world cases and hands-on group activities. 
  • Weekly Self-Paced Coursework: Weekly pre-recorded lectures will be provided for all modules. The length of these recordings will be from 30 to 60 minutes long. 
  • Capstone Case Study: Students will have the opportunity to develop a real or hypothetical cybersecurity machine learning deployment case study, culminating in personalized UChicago faculty feedback and guidance on your strategy.
  • Optional Networking and Offices Hours: Connect with peers in your industry through virtual happy hours and discuss your cybersecurity strategy with instructors through virtual office hours. 

To be best prepared to succeed in this program, students should have basic familiarity with:

  • Programming: Proficiency with one or more programming languages such as Python/C/C++/MATLAB/Java/JavaScript 
  • Basic Probability and Statistics: You should know the basics of probabilities, gaussian distributions, mean, and standard deviation
  • Linear Algebra: You should be comfortable with matrix/vector notation and operations
  • Computer Security: Basic knowledge of cybersecurity or applied computer security

Week 1: March 29–April 2

Class Session: March 30, 7:00–9:00 p.m. CDT

Virtual Happy Hour: 9:00–10:00 p.m. CDT

  • Introduction to supervised learning models
    • Logistic regression, Naive Bayes, neural networks, deep learning models 
  • Introduction to unsupervised learning models
    • PCA, K-means, Gaussian mixture models
  • Live Demonstration: Building a machine learning pipeline

Week 2: April 5–9

Class Session: April 6, 7:00–9:00 p.m. CDT

Virtual Office Hours: 9:00–10:00 p.m. CDT

  • Introduction to Internet architecture, measuring Internet traffic behavior and anomaly detection
  • Live Demonstration: Analyze internet network traffic using unsupervised learning techniques
  • Applications of machine learning to network security
    • Supervised learning examples: Spam filtering, phishing
    • Unsupervised learning examples: Anomaly detection

Week 3: April 12–13

Class Session: April 13, 7:00–9:00 p.m. CDT

  • Introduction to adversarial machine learning, threat models
    • Example: Distorting personalization
  • Defending against adversaries
  • Example: Evading intrusion/attack detection

Week 4: April 19–23

Class Session: April 20, 7:00–9:00 p.m. CDT

  • Fairness, Transparency, and Explainability in cybersecurity ML models
    • Privacy definitions and how to actualize privacy for cybersecurity applications in industry
    • Externalities and implications of errors in ML models for cybersecurity 
  • Responsible data lifecycles
    • Hands-on lab focused on building a model to detect fraudulent accounts leveraging virtual case study

Week 5: April 26–30

Class Session: April 27, 7:00–9:00 p.m. CDT

  • Students have the option to develop a real or hypothetical cybersecurity machine learning deployment case study, culminating in personalized UChicago faculty feedback and guidance on your strategy.
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Machine Learning Actionable Frameworks

Actionable Frameworks

Develop a holistic approach to data science focused on foundational topics.

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Machine Learning Context

Real-world Context

Apply foundational techniques to contemporary case studies, complex datasets, and unique business challenges.

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Machine Learning Experts

Engage with Experts

Learn from and network with leading faculty and colleagues across the science and technology sectors.

About the Center for Data and Computing

The Center for Data and Computing (CDAC) is an intellectual hub and incubator for data science and artificial intelligence research at the University of Chicago. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. As the focal point for data science research on campus, CDAC engages leaders from industry, government, and academia through innovative events and partnerships to spark new collaborations and technological discoveries.

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Sayeeda Khan, Assistant Director of Enrollment Management at UChicago

Tuition Support and Corporate Group Discounts

Corporate group enrollment discounts and tuition support are available. Contact Sayeeda Khan, Assistant Director of Enrollment Management, to learn more.

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Offered by The University of Chicago's Center for Data and Computing

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