- Tytuł:
- Fast attack detection method for imbalanced data in industrial cyber-physical systems
- Autorzy:
-
Huang, Meng
Li, Tao
Li, Beibei
Zhang, Nian
Huang, Hanyuan - Powiązania:
- https://bibliotekanauki.pl/articles/23944834.pdf
- Data publikacji:
- 2023
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
intrusion detection system
industrial cyber-physical Systems
imbalanced data
all k-nearest neighbor
LightGBM - Opis:
- Integrating industrial cyber-physical systems (ICPSs) with modern information technologies (5G, artificial intelligence, and big data analytics) has led to the development of industrial intelligence. Still, it has increased the vulnerability of such systems regarding cybersecurity. Traditional network intrusion detection methods for ICPSs are limited in identifying minority attack categories and suffer from high time complexity. To address these issues, this paper proposes a network intrusion detection scheme, which includes an information-theoretic hybrid feature selection method to reduce data dimensionality and the ALLKNN-LightGBM intrusion detection framework. Experimental results on three industrial datasets demonstrate that the proposed method outperforms four mainstream machine learning methods and other advanced intrusion detection techniques regarding accuracy, F-score, and run time complexity.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 229--245
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł