@inproceedings{11de56ff0f9f43f7afd4db8a89218463,
title = "Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results",
abstract = "The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices. The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices. The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices. The paper presents the background of the competition, the top models submitted, and the competition results. The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems.",
keywords = "KnowledgePit.ai platform, ML applications in log analysis, ML data quality, cybersecurity, data science competitions",
author = "Michal Czerwinski and Marcin Michalak and Piotr Biczyk and Blazej Adamczyk and Daniel Iwanicki and Iwona Kostorz and Maksym Brzeczek and Andrzej Janusz and Marek Hermansa and Lukasz Wawrowski and Artur Kozlowski",
note = "Publisher Copyright: {\textcopyright} 2023 Polish Information Processing Society.; 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023 ; Conference date: 17-09-2023 Through 20-09-2023",
year = "2023",
doi = "10.15439/2023F3089",
language = "English",
series = "Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1289--1293",
editor = "Maria Ganzha and Leszek Maciaszek and Leszek Maciaszek and Marcin Paprzycki and Dominik Slezak and Dominik Slezak and Dominik Slezak",
booktitle = "Proceedings of the 18th Conference on Computer Science and Intelligence Systems, FedCSIS 2023",
address = "United States",
}