A Survey of Trust Management Schemes for Social Internet of Things

Kuseh, None Simon Wewoliamo; Nunoo‐Mensah, Henry; Klogo, Griffith Selorm; Tchao, Eric Tutu

doi:10.25139/inform.v7i1.4284

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…Everyday objects can connect and exchange data with other devices and computer systems via IoT networks by means of sensors, processors, applications, and other technologies that are integrated into them.These social connections enable interaction between devices and people, aid their social networks, and make information sharing easier. IoT reuses the principles and concepts of human social networking to address IoT-related issues.The models now used in human social networks can therefore be used to solve IoT-related problems [4].Although security solutions have advanced, they still have issues with scalability, centralization, and unclear design. Additionally, they overemphasise the physical elements of the gadgets while ignoring the social intelligence that comes from interactions between the gadgets and their users.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Convolutional Neural Network based network intrusion detection and classification method for Social Internet of Things

Maheswaravenkatesh

2024

Preprint

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The Internet of Things (IoT) and social networking principles have been combined to create the so-called Social Internet of Things paradigm, which holds that devices can build social connections with their owners on their own. In this scenario, "things" engage with their peers in order to find the services they require.If the IoT is not handled properly, assaults and issues could overshadow any advantages.The quantity and methods of attacks have, however, expanded due to the extraordinary improvement of this technology. The complexity of maintaining data privacy as a result makes it even more challenging to offer top-notch services and complete security.The development of intrusion detection systems (IDS), which can quickly and accurately identify and categorise intrusions at various levels of networks, has made substantial use of deep learning techniques. In this work, a network intrusion detection model based on convolutional neural networks that has five convolutional layers is proposed. This model is tested using the CICIDS2018 dataset, a publicly accessible dataset with 80 statistical features, for both binary and multi-class classification. Data transformation and numerical standardisation procedures are used to pre-process the dataset.Experiments are conducted to assess the performance of the proposed system, and the results of the study demonstrate that the proposed CNN outperforms existing intrusion detection techniques in terms of multi-class categorization detection, with average values for accuracy, precision, recall, and F1-score of 99.65%, 99.16%, 98.70% and 99.09%, respectively.

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Section: Introductionmentioning

confidence: 99%

A Lightweight Convolutional Neural Network based network intrusion detection and classification method for Social Internet of Things

Maheswaravenkatesh

2024

Preprint

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“…When tackling IoT-related problems, IoT recycles the principles and ideas of human social networking. Therefore, IoT-related issues can be solved using models that are now employed in human social networks (Kuseh et al 2022 ).Security solutions have improved, yet they continue to suffer scalability, centralization, and confusing design problems. Furthermore, they neglect the social intelligence that results from interactions between the gadgets and their users and instead place too much emphasis on their physical aspects.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning-based approach for detecting attacks in social IoT

Das¹,

Kumar

Gopinath

et al. 2023

Soft Comput

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In the innovative concept of the “Social Internet of Things” (IoT), the IoT is combined with social platforms so that inanimate devices can form their interactions with one another. Still, customers have a wary attitude toward this new standard. They worry that their privacy will be invaded and their information will be made public. IoT won't become a frontrunner technology until we have tried true techniques to improve trustworthy connections between nodes. As a result, data privacy becomes extremely difficult, further increasing the difficulty of providing high-quality services and absolute safety. Several articles have attempted to analyze this issue. To categorize safe nodes in the IoT network, they suggested many models based on various attributes and aggregation techniques. In contrast, prior works failed to provide a means of identifying fraudulent nodes or distinguishing between different forms of assaults. To identify attacks carried out by hostile nodes and separate them from the network, we propose a novel Multi-hop Convolutional Neural Network with an attention mechanism (MH-CNN-AM). To achieve the best performance in the suggested research, performance measures including accuracy, precision, recall, F 1-score, and MAE are studied and compared with the of existing methodologies.

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A Survey of Trust Management Schemes for Social Internet of Things

Cited by 2 publications

References 42 publications

A Lightweight Convolutional Neural Network based network intrusion detection and classification method for Social Internet of Things

A Lightweight Convolutional Neural Network based network intrusion detection and classification method for Social Internet of Things

A novel deep learning-based approach for detecting attacks in social IoT

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