Quantum ML Challenge Highlights DDoS Detection Limitations

• •The ETH Quantum Hackathon focused on DDoS detection using quantum machine learning.
• •Classical techniques outperformed quantum methods in clean data scenarios.
• •Quantum methods may excel in complex, ambiguous attack situations.

From May 8-10, 2026, the GSMA and QCentroid hosted a challenge at the ETH Quantum Hackathon in Zürich, focusing on using quantum machine learning to detect distributed denial of service (DDoS) attacks. Teams analyzed network telemetry data modeled after a university campus network, simulating two attack patterns: internal machines flooding a target and cloud-hosted bots. Participants were tasked with identifying attack windows and source IP addresses, while being judged on their methodological justification for using quantum techniques. Results indicated that classical methods were more effective with clean data, but quantum methods showed potential in complex scenarios with limited datasets or novel attack types. The findings suggest that while quantum ML has promise, it currently does not surpass classical methods in straightforward applications. The event provided insights into the current limitations and potential future directions for quantum machine learning in cybersecurity.