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We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). Situated Analytics allows for the anchoring of city information in its spatial context. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. However, interaction requires appropriate interfaces to address the complexity of the city. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions.
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While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life.
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Finally, we establish reasons for the complete restructuring of the responsibilities, requirements, and proactive options for implementing cybersecurity rules by IoT device manufacturers. We detail the impact of such security and privacy vulnerabilities by conducting three case studies on IoT smart toys, including FisherPrice's SmartBear, Spiral Toys CloudPet Unicorn, and Owl's SmartWatch. In this paper, we review the currently existing regulatory and legal controls related to IoT devices while giving a brief overview of privacy & security policies that govern the data access, retention, and usage policies of children's smart toys. The most vulnerable of these users are children, who are at the most significant risk of harm and least adequately protected by the current regime of controls for devices such as smart toys. Given the ubiquitous nature of IoT devices, current cybersecurity and privacy laws fail to enforce the protections of the data of vulnerable populations. The current set of reactive regulatory agencies, legal protections, and market forces have proven inadequate for managing the security and privacy of the Internet of Things (IoT). The findings were also benchmarked with the existing works, and our results were competitive with an accuracy of 99.9% and MCC of 99.97%. The experimental results of our findings were evaluated in terms of validation dataset, accuracy, the area under the curve, recall, F1, precision, kappa, and Mathew correlation coefficient (MCC). Lastly, six proposed machine learning models were used for the analysis. In the next stage, dimensionality reduction was performed with Principal Component Analysis (PCA). This dataset is a mixture of contemporary attacks and normal activities of network traffic grouped into nine different attack types. In the first stage of this research methodology, feature scaling was done using the Minimum-maximum (min–max) concept of normalization on the UNSW-NB15 dataset to limit information leakage on the test data. The primary objective of this research focuses on applying ML-supervised algorithm-based IDS for IoT.
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This paper proposes a machine learning-based intrusion detection system (ML-IDS) for detecting IoT network attacks.
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Therefore, how to improve the security and privacy challenges of IoT remains an important problem in the computer security field. Unfortunately, the striking challenge of IoT is the privacy and security issues resulting from the energy limitations and scalability of IoT devices. Where the IoT applications became well known for technology researchers and developers. The adoption of IoT in the different sectors, including health, has also continued to increase in recent times. By comparing the highest rates of accuracy, institutions are picking intelligent procedures for testing and verification. Companies are increasing their investment in research to improve the detection of these attacks. Various and sophisticated intrusions are driving the IoT paradigm into computer networks. The introduction of varied devices continues to grow tremendously, posing new privacy and security risks-the proliferation of Internet connections and the advent of new technologies such as the IoT. The Internet of Things (IoT) refers to the collection of all those devices that could connect to the Internet to collect and share data.