Internet of Things最新文献

The Social Internet of Things (SIoT) amalgamates social networks with the Internet of Things (IoT) to enable intelligent devices to form social connections analogous to human networks. This research is grounded in psychological contract theory, which examines the reciprocal mechanisms arising from diverse customer interactions to encourage user engagement and provide recommendations on social media platforms. This study in particular identifies the factors that drive customer engagement on social media. It is unique in its exploration of customer interactions within the framework of psychological contracts across multiple levels of customer engagement (through customer empowerment). The findings reveal that psychological ownership among customers is influenced by empowering interactions on social media, which ultimately drive engagement behaviors.

Distributed Denial of Service (DDoS) attacks have always been an important research topic in the field of information security. Regarding specialized infrastructures such as electric vehicle charging stations, detecting and preventing such attacks becomes even more critical. In the existing literature, most studies on DDoS attack detection focus on traditional methods that analyze network metrics such as network traffic, packet rates, and number of connections. These approaches attempt to detect attacks by identifying anomalies and irregularities in the network, but can have high error rates and fail to identify advanced attacks. Conversely though, detection methods based on system metrics use deeper and more insightful parameters such as processor utilization, memory usage, disk I/O operations, and system behavior. Such metrics provide a more detailed perspective than network-based approaches, allowing for more accurate detection of attacks. However, work in this area is not yet widespread enough further research and improvement are needed. The adoption of advanced system metrics-based methods can significantly improve the effectiveness of DDoS defense strategies, especially in next-generation and specialized infrastructures. This paper evaluates the applicability and effectiveness of Long Short-Term Memory (LSTM) and Feed-Forward Network (FFN) in detecting DDoS attacks against electric vehicle charging stations through system metrics using CICEV2023 dataset. Experimental results show that the LSTM based model offers advantages in terms of speed and processing capacity, while the FFN is superior in terms of the accuracy.

Sharing Cyber Threat Intelligence (CTI) enables organisations to work together to defend against cyberattacks. However, current methods often fail to adequately protect sensitive information, leading to security risks, especially in Intelligent Additive Manufacturing (IAM) systems. In these systems, the security and privacy of incident data collected by IoT devices are essential, as revealing threat information, such as types, impacts, and organisational interests, could be harmful. To address these challenges, we propose the Severity Pattern Matching for a Secure Computable Threat Information Sharing System (SPM-SeCTIS). This system is designed to maintain privacy by allowing intermediaries to pass along threat information without accessing sensitive details, such as the type or severity of the threats. SPM-SeCTIS ensures that attackers cannot determine which incidents organisations are interested in or what specific threats they monitor. The system employs Homomorphic Encryption (HE) to conduct threat pattern matching on encrypted data, keeping sensitive information confidential even during analysis. Our performance tests indicate that SPM-SeCTIS operates efficiently, requiring minimal time for encryption and decryption processes. Additionally, the system scales effectively, handling a large number of subscribers and incidents with ease. Compared to existing methods, SPM-SeCTIS provides improved security measures and better overall performance, making it a robust solution for protecting sensitive threat information.

The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.

Aerial edge computing (AEC) has emerged as a pivotal platform offering low-latency computation services, seamless deployability, rapid operationality, and high maneuverability to the Internet of things (IoT) devices of end users. Different aerial computing platforms offer different computation support to process the tasks of IoT devices, which affects the offloading decision. However, effective task offloading (TO) decision-making in this context remains a critical challenge because it impacts the quality of service, energy consumption, resource allocation, and latency requirements. Most current research uses reinforcement learning (RL)-based offloading decisions in AEC owing to the uncertainty of the environment and the heterogeneity of computation platforms. Therefore, this survey explores the prevailing use of RL-based algorithms for TO in AEC, addressing the inherent uncertainty of the environment and the heterogeneity of computation platforms. This study systematically reviews and compares RL-based techniques employed for efficient offloading decisions in heterogeneous aerial computing platforms. It delves into recent research findings, highlighting the various approaches and methodologies applied. Additionally, the paper provides a comprehensive overview of the performance metrics widely used to evaluate the efficacy of RL-based offloading decision techniques. In conclusion, this survey identifies research gaps and outlines future directions, aiming to guide scholars and practitioners in advancing the field of RL-based TO in AEC.

Following the establishment of the draft standardization for Post-Quantum Cryptography (PQC), cryptographic systems across various sectors have undergone a paradigm shift. Although the theoretical strength of PQC has provided a robust foundation for securing communications against quantum threats, physical implementations of PQC algorithms remain vulnerable to Side-Channel Attacks (SCAs). Existing SCA studies predominantly focus on the attack process, lacking thorough side-channel leakage assessments and comparisons of inherent vulnerabilities at different attack points and with different countermeasures. In this paper, we first present a comprehensive assessment of side-channel leakage and resistance of four attack points within an ARM Cortex-M4 implementation of Kyber, including its masked version. This assessment employs a range of countermeasures such as noise addition, random delays, clock jitter, and their combinations. Besides, we also build deep-learning models for attacking, thereby verifying the results of the leakage assessments. By collaboratively utilizing three distinct leakage assessment approaches and deep learning-based attack results, we experimentally demonstrate that different algorithmic intermediate values of Kyber are suited to different countermeasures, which advances our understanding of the capacity and vulnerability of PQC implementations.

Recent trends in emerging applications have motivated researchers to design advanced wireless systems to meet their evolving requirements. These emerging applications include digital healthcare, intelligent transportation systems, Industry 5.0, and more. To address the evolving requirements, leveraging a metaverse to empower 6G wireless systems is a viable solution. A metaverse-empowered 6G wireless system can offer numerous benefits, but it may also be vulnerable to a wide variety of security attacks. In this survey, we discuss potential security attacks in metaverse-empowered 6G wireless systems. We introduce an architecture designed to enhance security within metaverse-empowered 6G wireless systems. This architecture comprises two key spaces: the meta space and the physical space. We present physical space attacks and outline effective solutions to secure metaverse-empowered 6G wireless systems. We provide invaluable insights and discussions on meta space attacks, along with promising solutions. Finally, we discuss open challenges and provide future recommendations.

Ubiquitous sensing in smart cities is expected to be one of the key beneficiaries of the high bandwidth and low latency capabilities of 5G. However, current 5G deployments still have low population coverage, and are unlikely to reach global coverage of above 80% before 2028. This means that new smart city applications are likely to experience a combination of 4G and 5G, limiting their data-intensive capabilities. Thus, it is necessary to assess the ability of current 5G deployments to support emerging smart city applications, and how they perform in 5G environments. Existing performance evaluations focus either on the 5G core or use instantaneous speed tests, which do not effectively assess the suitability of 5G deployments for smart city applications. In this paper, we present a comprehensive evaluation and analysis of real-world 5G network performance observed through the outcomes of a pilot smart city application, an innovative mobile 5G Internet of Things (IoT) solution to automatically identify and report road assets requiring maintenance. The pilot smart city application was deployed on 11 waste collection service trucks over a 6 month period (June 2022–Dec 2022). We undertook both application-specific and application independent network performance evaluation to assess the ability of the 5G deployment to support uninterrupted smart city services. Our analysis shows that while 5G is capable of supporting mobile video streaming applications, there are significant variations in network performance, which may make it unsuitable for applications that require higher data intensive or near real-time responsiveness.

The proliferation of Internet of Things (IoT) systems across diverse applications has led to a notable increase in connected smart devices. Nevertheless, this surge in connectivity has induced a broad spectrum of vulnerabilities and threats, jeopardizing the security and safety of IoT applications. Security risk assessment methods are commonly employed to analyze risks. However, traditional IT and existing IoT-tailored security assessment methods often fail to fully address key IoT aspects: complex assets intercommunication, dynamic system changes, assets’ potential as attack platforms, safety impacts of security breaches, and assets resource constraints. Such oversights lead to significant risks being overlooked in the IoT ecosystem. In this paper, we propose a novel vulnerability-oriented risk identification framework comprising a four-step process as a core element of IoT security risk assessment, applicable to any IoT system. Our process enhances both traditional and IoT-specific security risk assessment methods by providing tailored approaches that address their crucial oversights for comprehensive IoT risk assessment. We validate our process with a case study of an IoT smart healthcare system using a proposed expert-driven approach. The results confirm that our process effectively identifies critical attack scenarios originating from the lack of proper security measures, mobility, and intercommunication processes of IoT devices in the healthcare system. Furthermore, our analysis reveals potential attacks that exploit the IoT devices as platforms to target the backend and user domains. We demonstrate the feasibility of our process for identifying realistic risks by conducting simulations of two derived attack scenarios using the Contiki Cooja network simulator.

In the Internet of Things (IoTs) and wireless sensor networks (WSNs), improving security and energy efficiency are key concerns. Clustering, which involves managing cluster heads, plays a pivotal role in extending network lifetime. The selection of a cluster head, responsible for data transfer between nodes, is a key aspect of network management. This paper proposes two variants of a novel algorithm designed for energy efficient communication in a resource constrained IoT environments. One variant considers remaining energy, distance, and node degree for cluster head selection, while the other focuses on remaining energy and distance only. Including node degree ensures cluster heads do not waste energy by remaining idle or performing unnecessary tasks such as the cluster head selection process in every round. The authors tested these variants against several well known algorithms using MATLAB simulation environment, evaluating factors such as operating nodes, number of clusters, transmission energy, and remaining energy. The proposed algorithm extends network lifetime by maintaining more operating nodes for longer, not changing clusters or cluster heads frequently, minimizing energy consumption for transmission, and conserving more remaining energy. Consequently, the proposed algorithm outperforms existing approaches by addressing issues like zero cluster head selection, compulsory cluster head selection in every round, avoiding cluster heads that connect to no nodes, and preventing network destabilization due to unnecessary re-elections.