Home » Case Studies » Machine Learning Accelerates Cyber Security


As society moves more services and data online, the attack surface for cyber criminals increases – presenting a major risk to organisations.

When cybersecurity start-up, Cydarm Technologies, identified alert triage as the key to efficient incident response, the team collaborated with APR.Intern to enlist the skills of Federation University Australia IT PhD candidate, Paul Black.

With over 30 years of technical experience in industry, mature-age student, Paul was able to bring both new and well-seasoned programming approaches to the internship. The result was an innovative machine learning system, automating the severity assessment of security alerts.

“The project really helped to identify the transferability of my new skills and modernise my thinking,” 


Paul Black, PhD Intern at Cydarm Technologies

“Machine learning has become a critical aspect of cyber security research and the opportunity to put new theory to practice improved my techniques,” Paul added.

For Cydarm CEO & Founder, Vaughan Shanks, welcoming an APR intern allowed his business to rapidly develop a new end-to-end system, increasing efficiency via automated alert triage.

“Initial testing demonstrated accurate predictions and fine-tuning will cement validation for external use. The system’s delivery within a short time frame was a great success,”


Vaughan Shanks, CEO Cydarm Technologies

Cydarm Technologies saw value in Paul’s skills and extended the project from four to five-months to see completion.

Following his time at Cydarm Technologies, Paul was offered a security researcher position at the Internet Commerce Security Lab (ICSL) in Federation University Australia where he will draw from his experience solving industry problems with computing expertise.

This internship was supported by both the Australian Government and Defence Science Institute.