A distributed intelligence framework for enhancing resilience and data privacy in dynamic cyber-physical systems

被引:3
作者
Azeri, Nabila [1 ]
Hioual, Ouided [2 ]
Hioual, Ouassila [2 ,3 ]
机构
[1] Abbes Laghrour Univ, ICOSI Lab, BP 1252 Rd Batna, Khenchela 40004, Algeria
[2] Abbes Laghrour Univ, Math & Informat Dept, BP 1252 Rd Batna, Khenchela 40004, Algeria
[3] Constantine 2 Univ, LIRE Lab, BP67A, Ali Mendjeli, Constantine 25000, Algeria
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2024年 / 27卷 / 05期
关键词
Cyber-physical systems; Multi-layer architecture; Federated machine learning; Fault prediction;
D O I
10.1007/s10586-024-04349-y
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cyber-Physical Systems (CPS) are integral components of modern, interconnected environments, playing a crucial role in various applications such as smart infrastructure and autonomous systems. These systems operate in complex and ever-evolving settings, where resilience is an essential characteristic. Recently, several Machine Learning (ML) techniques have been proposed to design and implement resilience in CPS. However, this approach has often prioritized performance gains and adaptability enhancements, leading to a disregard for the potential dangers linked to centralized data processing, including data breaches and privacy infringements. To harness the full potential of ML in CPS, this paper introduces a distributed intelligence framework that equally prioritizes security, data privacy, and adaptability. The proposed framework is implemented through the integration of Federated Machine Learning techniques, where the CPS architecture is decentralized, allowing data processing to occur locally on individual nodes or devices. This decentralized approach facilitates the aggregation of insights from multiple sources without the need for centralized data processing, thereby minimizing the risks associated with data breaches and privacy violations. We further validate the viability of the proposed framework through its successful implementation in a real-world industrial CPS application, specifically focused on fault prediction within industrial CPS environments. In addition to its privacy and security benefits, our approach also achieved promising results in terms of accuracy and precision.
引用
收藏
页码:6289 / 6304
页数:16
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