Data mining competition
FedCSIS 2023 Challenge: Cybersecurity Threat Detection in the Behavior of IoT Devices is the 9th data science competition in the series. This year, the task is related to cybersecurity – participants will construct scoring models to detect anomalous operating system behavior of IoT devices under attack. The challenge is sponsored by Łukasiewicz Research Network – Institute of Innovative Technologies EMAG and EFIGO sp. z o.o. companies.
The competition is held at the KnowledgePit online platform managed by QED Software – the company which is specialized in the development and deployment of innovative AI / ML / Big Data products and solutions.
Awards
Authors of the top-ranked solutions (based on the final evaluation scores) will be awarded prizes funded by the Sponsors:
- 1000 USD for the winning solution + 600 EUR for one FedCSIS 2023 registration
- 500 USD for the 2nd place solution + 600 EUR for one FedCSIS 2023 registration
- 250 USD for the 3rd place solution + 600 EUR for one FedCSIS 2023 registration
Schedule
- June 2, 2023 (23:59 GMT): deadline for submitting the predictions
- June 4, 2023 (23:59 GMT): deadline for sending the reports, end of the competition
- June 09, 2023: online publication of the final results, sending invitations for submitting short papers for the special session at FedCSIS'23
- July 09, 2023: deadline for submitting invited papers
- July 16, 2023: notification of paper acceptance
- July 28, 2023: camera-ready of accepted papers, and registration for the conference are due
September 18, 2023 (Monday), 17:00-18:30, room 170
| 17:00–17:10 | Introductory talk on behalf of competition sponsors by Marcin Michalak |
| 17:10–17:30 | Michał Czerwiński Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results |
| 17:30–17:50 | Dymitr Ruta, Ming Liu and Ling Cen Beating Gradient Boosting: Target-Guided Binning for Massively Scalable Classification in Real-Time |
| 17:50–18:10 | Sławomir Pioroński and Tomasz Górecki Spotting Cyber Breaches in IoT Devices |
| 18:10–18:30 | Ming Liu, Ling Cen and Dymitr Ruta Gradient boosting models for cybersecurity threat detection with aggregated time series features |
