000150137 001__ 150137
000150137 005__ 20250131123450.0
000150137 0247_ $$2doi$$a10.1016/j.dib.2024.111153
000150137 0248_ $$2sideral$$a142426
000150137 037__ $$aART-2024-142426
000150137 041__ $$aeng
000150137 100__ $$aGutiérrez Mlot, Esteban Damián
000150137 245__ $$aA dataset to train intrusion detection systems based on machine learning models for electrical substations
000150137 260__ $$c2024
000150137 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150137 5203_ $$aThe growing integration of Information and Communication Technology into Operational Technology environments in electrical substations exposes them to new cybersecurity threats. This paper presents a comprehensive dataset of substation traffic, aimed at improving the training and benchmarking of Intrusion Detection Systems (IDS) installed in these facilities that are based on machine learning techniques. The dataset includes raw network captures and flows from real substations, filtered and anonymized to ensure privacy. It covers the main protocols and standards used in substation environments: IEC61850, IEC104, NTP, and PTP. Additionally, the dataset includes traces obtained during several cyberattacks, which were simulated in a controlled laboratory environment, providing a rich resource for developing and testing machine learning models for cybersecurity applications in substations. A set of complementary tools for dataset creation and preprocessing are also included to standardize the methodology, ensuring consistency and reproducibility. In summary, the dataset addresses the critical need for high-quality, targeted data for tuning IDS at electrical substations and contributes to the advancement of secure and reliable power distribution networks.
000150137 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T21-23R$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131115A-I00
000150137 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000150137 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150137 700__ $$aSaldana, Jose
000150137 700__ $$0(orcid)0000-0001-7982-0359$$aRodríguez, Ricardo J.$$uUniversidad de Zaragoza
000150137 700__ $$aKotsiuba, Igor
000150137 700__ $$aGañán, Carlos
000150137 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000150137 773__ $$g57 (2024), 111153 [11 pp.]$$pData brief$$tData in Brief$$x2352-3409
000150137 8564_ $$s1581298$$uhttps://zaguan.unizar.es/record/150137/files/texto_completo.pdf$$yVersión publicada
000150137 8564_ $$s1368055$$uhttps://zaguan.unizar.es/record/150137/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150137 909CO $$ooai:zaguan.unizar.es:150137$$particulos$$pdriver
000150137 951__ $$a2025-01-31-12:02:59
000150137 980__ $$aARTICLE