Detekcija False Data Injection napada u pametnim mrežama primenom mašinskog učenja na Big Data setovima

Energija, ekonomija, ekologija, 2, XXVIII (2026) (40-45 str.)

 

АУТОР(И) / AUTHOR(S): Vladimir Bečejac , Dobrila Škatarić , Petar Lukić

 

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DOI: https://doi.org/10.46793/EEE26-2.40B

САЖЕТАК / ABSTRACT:

Digitalizacija elektroenergetskog sistema omogućava efikasnije upravljanje, ali otvara prostor za sofisticirane sajber napade, poput ubacivanja lažnih podataka. Tradicionalne metode detekcije loših podataka često nisu u stanju da prepoznaju napade koji su dobro programirani i ostaju unutar granica greške estimacije stanja. Ovaj rad daje pristup detekciji lažnih napada primenom algoritama mašinskog učenja u MATLAB okruženju. Koristeći velike setove podataka razvijen je model koji identifikuje anomalije u korelacijama mrežnih parametara u realnom vremenu. Rezultati simulacija na IEEE test sistemima pokazuju visoku preciznost modela u razlikovanju normalnih promenama podataka od zlonamernih manipulacija podacima, čime se direktno unapređuje pouzdanost i odgovorna upotreba digitalne infrastrukture EES.

КЉУЧНЕ РЕЧИ / KEYWORDS:

FDIA, mašinsko učenje, estimacija stanja, big data

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

Prikazani rezultati su rezultat istraživanja podržanog od strane Ministarstva nauke, tehnološkog razvoja i inovacija RS po Ugovoru 451-03-34/2026-03/ 200105 оd 05.02.2026. god.

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