As the complexity of ICS increases, security concerns also grow. Anomalies may indicate failures or cyberattacks, making early detection crucial. Traditional statistical and threshold-based methods are ineffective in dynamic ICS environments. ML- and DL-based methods, including support vector machines, autoencoders, and recurrent neural networks, improve accuracy and adaptability. However, they often require high computational resources. Transfer learning, federated learning, and XAI help overcome these limitations. This study analyzes and compares traditional and emerging approaches.
| Mualliflar | Jumayev, Sodiq, G‘ulomov , Sherzod |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-03-23 |
| Son | 1 |
| Betlar | 92-99 |
| Til | Rus |
Обнаружение аномалий, информационно-коммуникационные системы, машинное обучение, глубокое обучение, гибридные модели, трансферное обучение, федеративное обучение, объяснимый ИИ, мониторинг в реальном времени, кибербезопасность, Anomaly detection, information communication systems, machine learning, deep learning, hybrid models, transfer learning, federated learning, explainable AI, real-time monitoring, cybersecurity., Anomaly detection, information communication systems, machine learning, deep learning, hybrid models, transfer learning, federated learning, explainable AI, real-time monitoring, cybersecurity
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