This article proposes an approach for detecting and identifying Stuxnet-type cyberattacks in modern Internet of Things (IoT) systems. A model of informative features has been developed, and a knowledge base for detecting such attacks using machine learning methods has been formalized. The proposed approach is based on the analysis of Stuxnet malware characteristics and its adaptation to IoT infrastructure conditions. Experiments were conducted to evaluate the effectiveness of the proposed methods and algorithms. The results demonstrate a high detection accuracy (94.7%) of Stuxnet-type attacks in IoT systems, as well as the capability to identify previously unknown modifications of such attacks.
| Mualliflar | O'rinov, Nodirbek, Мамадалиев, Сарварбек, Бахрамова, Махлиёхон, Aлижонов , Шохислом, Неъматжонов , Aсадбек, Aкбарова , Мадина |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-06-03 |
| Son | 2 |
| Betlar | 80-86 |
| Til | Rus |
Stuxnet, Интернет вещей (IoT), информативные признаки, машинное обучение, кибербезопасность, целенаправленные кибератаки, промышленные системы управления, Stuxnet, Internet of Things (IoT), informatsion xususiyatlar, mashinani o'rganish, kiberxavfsizlik, maqsadli kiberhujumlar, sanoat boshqaruv tizimlari
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