A Detection Framework Against CPMA Attack Based on Trust Evaluation and Machine Learning in IoT Network

Liu, Liang; Xu, Xiangyu; Liu, Yulei; Ma, Zuchao; Peng, Jianfei

doi:10.1109/jiot.2020.3047642

Cited by 32 publications

(5 citation statements)

References 28 publications

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“…DS evidence rules fuse the training subclassifiers to obtain the overall trust classifier. Liu et al [19] proposed an effective detection mechanism for conditional packet manipulation attacks in IoT networks. To evaluate the trust value of each IoT device, a regression model is first trained based on the reputation of relevant routing paths.…”

Section: Related Workmentioning

confidence: 99%

Trust management for IoT devices based on federated learning and blockchain

Wang,

Li,

Zuo

2024

Preprint

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The rapid growth of IoT devices and the increasing demand for device interaction between different network partitions have significantly pressured IoT device management. To realize cross-domain trust integration between different partitioned devices, trust management becomes the key technology to realize cross-domain communication. However, trust management heavily relies on third-party entities, posing centralized risks. Therefore, we propose an IoT device trust management system based on federated learning and blockchain. By utilizing federated learning to assess device reputation ratings while safeguarding their privacy, the system stores reputation assessment results on the blockchain for authenticity and accuracy. The system’s decentralization is achieved using blockchain instead of a central server in federated learning. Additionally, we introduce a weighted aggre-gation model based on device attributes to obtain a more precise global model through weighted aggregation of local models in federated learning. Experimental results using a simulated dataset reflecting device characteristics demonstrate a device evaluation accuracy of 90.2%, validating the system’s effectiveness and feasibility.

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Section: Related Workmentioning

confidence: 99%

Trust management for IoT devices based on federated learning and blockchain

Wang,

Li,

Zuo

2024

Preprint

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“…K-Means Algorithm: In [120], the authors presented conditional packet manipulation attacks called targeted insider attacks. The proposed scheme maintains limited performance metrics of trust for each node, which show the possibility of initial attacks such as forwarding the packets with specific values.…”

Section: Techniques For Evaluation Of Assisting Trustmentioning

confidence: 99%

IoT trust and reputation: a survey and taxonomy

et al. 2023

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IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilised across the globe by the end of 2030. To maximise the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices’ features, such as device’s role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organise the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics,e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.

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“…In their work, node misbehaving is detected via k-means clustering. Targeting packet manipulation attacks, Liu et al [91] tackled a regression-based approach together with a clustering algorithm to classify nodes as benign or malicious. Yang et al [92] investigated the problem of outlier detection in WSNs with a fuzzy-based trust evaluation methodology, to achieve a balance between energy consumption and security in the CH election process.…”

Section: B Clustering Modelsmentioning

confidence: 99%

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

Lenard,

Collen,

Benyahya

et al. 2023

IEEE Access

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A Detection Framework Against CPMA Attack Based on Trust Evaluation and Machine Learning in IoT Network

Cited by 32 publications

References 28 publications

Trust management for IoT devices based on federated learning and blockchain

Trust management for IoT devices based on federated learning and blockchain

IoT trust and reputation: a survey and taxonomy

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

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