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With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios. [ABSTRACT FROM AUTHOR]

Recommender systems can help correlate information and recommend personalised services to users as a general information filtering tool. However, contextual factors significantly affect user behaviour, especially in the Internet of Things (IoT), which brings difficulties to modelling user preferences. In this paper, we propose a personalised context-aware re-ranking algorithm (p-CAR) in IoT. Our primary purpose is to improve the recommender performance from multiple metrics, such as precision, recall, diversity, and popularity. The core idea is to re-rank the ranking list using the user's preference behaviour under different contexts. The re-ranking process is an iterative selection process; each time an optimal item that meets the target criteria is selected from the candidate items and added to the re-ranked list. The selection of items depends on the given context and the user's interest in that context. User's preference and interest in contexts are both expressed by probability in our algorithm. In addition, we use a weight parameter to control the influence of contexts and model the contextual personalisation of different users through local personalisation parameters. We verify our algorithm through experiments on the real Movielens 100K dataset and show the performance advantage with the existing algorithm. [ABSTRACT FROM AUTHOR]

Data deduplication can solve the problem of resource wastage caused by duplicated data. However, due to the limited resources of Internet of Things (IoT) devices, applying data deduplication to IoT scenarios is challenging. Existing data deduplication frameworks for the IoT are prone to inefficiency or trust crises due to the random allocation of edge computing nodes. Furthermore, side-channel attacks remain a risk. In addition, after IoT devices store data in the cloud through data deduplication, they cannot share their data efficiently. In this paper, we propose a secure and efficient data deduplication framework for the IoT based on edge computing and blockchain technologies. In this scheme, we propose a model based on parallel use of three-layer and two-layer architectures and introduce the RAndom REsponse (RARE) scheme to resist side-channel attacks. We also design a label tree to realise one-to-many data-sharing, which improves efficiency and meets the needs of the IoT. In addition, we use blockchain to resist collusion attacks. Experiments were conducted to demonstrate that our framework has advantages over similar schemes in terms of communication cost, security and efficiency. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices require lower power consumption with higher security, which can be achieved by using lightweight cryptography (LWC) approaches. Attribute-based encryption (ABE) provides a fine-grained access control policy over encrypted data, making it useful in IoT-based cloud storage for allowed data protection. However, the conventional ABE approaches resulted in poor security performance against various attacks in the IoT environment. So, in this paper, the LWC-ABE method is proposed to enhance the security performance against various attacks in the IoT environment. The proposed LWC-ABE contains only multiple trusted authority environments, which is a bottleneck in IoT servers and IoT devices. The proposed LWC-ABE method supports high expressiveness, access policy updates, large attribute domains, and white box traceability properties. The simulation results shows that the proposed LWC-ABE resulted in reduced encryption and decryption times for multi users, different message sizes scenarios as compared to conventional approaches. The numerical outcomes of the proposed method are much better based on performance of encryption and decryption times as 0.000835 and 0.000310 respectively. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT) extends the provision of remotely managed services across different domains. At the same time, IoT devices primarily designed for home environments may also be installed within the premises of critical urban environments, such as government, banking and corporate domains, without proper risk evaluation. In this paper, we examine the effect of cascading attacks triggered by the integration of vulnerable smart lighting systems in critical domains. In particular, we utilise known vulnerabilities of smart lighting systems to demonstrate the potential risk propagation on popular installation domains found in smart cities and urban infrastructures and services. Based on validated vulnerabilities on popular off-the-shelf smart lighting systems, we set up realistic proof-of-concept connectivity scenarios for various urban infrastructures and domains. Using these scenarios, we evaluate the risk of cascading attacks, by applying a targeted risk assessment methodology for identifying and assessing IoT-enabled attacks. [ABSTRACT FROM AUTHOR]

As a fundamental service in near future, medical digital twin (MDT) is the virtual replica of a person. MDT applies new technologies of IoT, AI and big data to predict the state of health and offer clinical suggestions. It is crucial to secure medical digital twins through deep understanding of the design of digital twins and applying the new vulnerability tolerant approach. In this paper, we present a new medical digital twin, which systematically combines Haptic-AR navigation and deep learning techniques to achieve virtual replica and cyber–human interaction. We report an innovative study of the cyber–human interaction performance in different scenarios. With the focus on cyber resilience, a new solution of vulnerability tolerant is the must in the real-world MDT scenarios. We propose a novel scheme for recognising and fixing MDT vulnerabilities, in which a new CodeBERT-based neural network is applied to better understand risky code and capture cybersecurity semantics. We develop a prototype of the new MDT and collect several real-world datasets. In the empirical study, a number of well-designed experiments are conducted to evaluate the performance of digital twin, cyber–human interaction and vulnerability detection. The results confirm that our new platform works well, can support clinical decision and has great potential in cyber resilience. [ABSTRACT FROM AUTHOR]

Federated learning (FL) plays an important role in the development of smart cities. With the evolution of big data and artificial intelligence, issues related to data privacy and protection have emerged, which can be solved by FL. In this paper, the current developments in FL and its applications in various fields are reviewed. With a comprehensive investigation, the latest research on the application of FL is discussed for various fields in smart cities. We explain the current developments in FL in fields, such as the Internet of Things (IoT), transportation, communications, finance, and medicine. First, we introduce the background, definition, and key technologies of FL. Then, we review key applications and the latest results. Finally, we discuss the future applications and research directions of FL in smart cities. [ABSTRACT FROM AUTHOR]

While population aging has sharply increased the demand for nursing staff, it has also increased the workload of nursing staff. Although some nursing homes use robots to perform part of the work, such robots are the type of robots that perform set tasks. The requirements in actual application scenarios often change, so robots that perform set tasks cannot effectively reduce the workload of nursing staff. In order to provide practical help to nursing staff in nursing homes, we innovatively combine the LightGBM algorithm with the machine learning interpretation framework SHAP (Shapley Additive exPlanations) and use comprehensive data analysis methods to propose a service demand prediction model Fidan (Forecast service demand model). This model analyzes and predicts the demand for elderly services in nursing homes based on relevant health management data (including physiological and sleep data), ward round data, and nursing service data collected by IoT devices. We optimise the model parameters based on Grid Search during the training process. The experimental results show that the Fidan model has an accuracy rate of 86.61% in predicting the demand for elderly services. [ABSTRACT FROM AUTHOR]

Most of the recent research has focused on the Internet of Things (IoT) and its applications. The open interface and network connectivity of the interconnected systems under the IoT network make them vulnerable to hackers. A model has been proposed to identify and classify IoT routing attacks. To generate IoT routing datasets, the Cooja simulator is used at first. The IoT routing dataset is then augmented into larger volumes using ADASYN, which is also used to solve the class imbalance problems. A deep learning hybrid model based on a Long-Short-Term Memory (LSTM) network and adaptive Mayfly Optimization Algorithm (LAMOA) was presented for the classification of IoT attacks. The adaptive MOA adjusts the weights in the various layers of the LSTM network and the Fully Connected Layer with SoftMax Classification. As part of the validation process, the proposed model was also compared with benchmark datasets NSL-KDD, BoT-IoT, and IoT-23. Using benchmark datasets, LAMOA achieved 99.94% accuracy for multiclassifications, 99.92% accuracy for binary classifications, and 98.42% accuracy for real-time datasets. Compared to other models, our proposed model significantly improves the accuracy of each attack's classification by 5–10%. [ABSTRACT FROM AUTHOR]

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network. [ABSTRACT FROM AUTHOR]

Since sensor nodes in wireless sensor networks or Internet of things have limited resources, achieving secure data transmissions among nodes is a challenge. Hence, efficient key management schemes with lightweight ciphers are essential. Many asymmetric keys or public key mechanisms have been developed. However, they are unsuitable for secure group communications in wireless sensor networks, because sensor nodes are usually devoid of sufficient memory, CPU and bandwidth to deal with complex operations. In addition, only a few group key agreements are integrated into secure data transmissions. Therefore, a key agreement using the hierarchy-based cluster elliptic curve key agreement named HCECKA is proposed in this study to deal with secure data transmissions in wireless sensor networks. The presented security mechanism relies on elliptic curves instead of logarithmic curves, and utilises a lesser key length to accomplish similar security classification than Diffie-Hellman and Rivest Shamir Adleman cryptosystems. Simultaneously, the proposed scheme provides a rapid, efficient and dynamic group key synchronisation technique in plenty of sensor nodes with no need for reorganising the whole system key if members join or leave wireless sensor networks. [ABSTRACT FROM AUTHOR]