New U. of Chicago Machine Learning for Cybersecurity Certificate Gives Professionals Tools to Detect and Prevent Attacks
Machine learning has become an essential tool for IT security professionals seeking to detect and prevent attacks and vulnerabilities. The Center for Data and Computing (CDAC) convened a trio of University of Chicago computer science faculty to produce an innovative new remote Machine Learning for Cybersecurity certificate that will be offered for the first time this autumn.
Machine learning and cybersecurity are traditionally treated as separate subdomains of computer science study. But in practice, machine learning has become an essential tool for IT security professionals seeking to detect and prevent attacks and vulnerabilities. Just as new methods of extracting knowledge and making predictions from perpetually growing and changing data sets have revolutionized science and business, they are creating novel defenses—and in some cases, attacks—in the ongoing struggle between hackers and security teams.
Many of the mid-career professionals in cybersecurity built their skills at a time when machine learning was still an esoteric approach and data was not as integral for security operations. To remedy this knowledge gap, the Center for Data and Computing (CDAC) convened a trio of University of Chicago computer science faculty—Nick Feamster, Yuxin Chen, and Blase Ur—to produce an innovative new remote Machine Learning for Cybersecurity certificate that will be offered for the first time this autumn.
“Artificial intelligence has been taking over the security area in products, startups, and methods for the last ten years,” said Feamster, CDAC faculty director and Neubauer Professor of computer science. “We want to give professionals the skills to correctly apply these models to their data and solve various security problems that they’re dealing with every day, from malware and denial of service attacks to botnets and phishing scams.”
UChicago’s five-week remote certificate will teach information security managers, DevOps engineers, software developers, and system administrators the fundamentals of machine learning for security in various real-world situations. While many off-the-shelf machine learning tools promise to catch IT departments up to modern state-of-the-art defenses, applying those tools without understanding how they work can cause more harm than good.
The program consists of five modules:
- Foundations of Machine Learning for Security;
- Data-Driven Network and Computer Security;
- Machine Learning in the Presence of Adversaries;
- Ethics, Fairness, Responsibility, and Transparency in Data-Driven Cybersecurity.
- Secure Machine Learning Development and Deployment
Each module will be taught with a combination of video instruction, case studies, interactive Jupyter notebook exercises, and live group discussions.
“We want to paint a roadmap towards how these security professionals can think about solving problems,” said Chen, assistant professor of computer science. “So once they see data from a practical challenge, they can start analyzing their data and thinking about which models could work and which models are not applicable, reasoning from the bottom up to build out the system rather than trying out different approaches blindly.”
For more information on the Machine Learning for Cybersecurity certificate, visit mlccertificate.uchicago.edu. The certificate and research project are supported by a grant from the National Science Foundation EAGER program, which funds exploratory, early-stage research. Corporate group enrollment discounts and tuition support is available.
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