Internet of Things and Cyber-Physical Systems最新文献_第3页

Ransomware on cyber-physical systems: Taxonomies, case studies, security gaps, and open challenges 网络物理系统中的勒索软件：分类、案例研究、安全漏洞和公开挑战

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2024-01-06 DOI: 10.1016/j.iotcps.2023.12.001

Mourad Benmalek

引用次数: 0

Deep learning for cyber threat detection in IoT networks: A review 深度学习在物联网网络中的网络威胁检测:综述

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2023-10-10 DOI: 10.1016/j.iotcps.2023.09.003

Alyazia Aldhaheri, Fatima Alwahedi, Mohamed Amine Ferrag, Ammar Battah

{"title":"Deep learning for cyber threat detection in IoT networks: A review","authors":"Alyazia Aldhaheri, Fatima Alwahedi, Mohamed Amine Ferrag, Ammar Battah","doi":"10.1016/j.iotcps.2023.09.003","DOIUrl":"https://doi.org/10.1016/j.iotcps.2023.09.003","url":null,"abstract":"<div><p>The Internet of Things (IoT) has revolutionized modern tech with interconnected smart devices. While these innovations offer unprecedented opportunities, they also introduce complex security challenges. Cybersecurity is a pivotal concern for intrusion detection systems (IDS). Deep Learning has shown promise in effectively detecting and preventing cyberattacks on IoT devices. Although IDS is vital for safeguarding sensitive information by identifying and mitigating suspicious activities, conventional IDS solutions grapple with challenges in the IoT context. This paper delves into the cutting-edge intrusion detection methods for IoT security, anchored in Deep Learning. We review recent advancements in IDS for IoT, highlighting the underlying deep learning algorithms, associated datasets, types of attacks, and evaluation metrics. Further, we discuss the challenges faced in deploying Deep Learning for IoT security and suggest potential areas for future research. This survey will guide researchers and industry experts in adopting Deep Learning techniques in IoT security and intrusion detection.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 110-128"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49883531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data management method for building internet of things based on blockchain sharding and DAG 基于区块链分片和 DAG 构建物联网的数据管理方法

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2024-02-05 DOI: 10.1016/j.iotcps.2024.01.001

Wenhu Zheng, Xu Wang, Zhenxi Xie, Yixin Li, Xiaoyun Ye, Jinlong Wang, Xiaoyun Xiong

{"title":"Data management method for building internet of things based on blockchain sharding and DAG","authors":"Wenhu Zheng, Xu Wang, Zhenxi Xie, Yixin Li, Xiaoyun Ye, Jinlong Wang, Xiaoyun Xiong","doi":"10.1016/j.iotcps.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.01.001","url":null,"abstract":"<div><p>Sharding technology can address the throughput and scalability limitations that arise when single-chain blockchain are applied in the Internet of Things (IoT). However, existing sharding solutions focus on addressing issues like malicious nodes clustering and cross-shard transactions. Existing sharding solutions cannot adapt to the performance disparities of edge nodes and the characteristic of three-dimensional data queries in building IoT. This leads to problems such as shard overheating and inefficient data query efficiency. This paper proposes a dual-layer architecture called S-DAG, which combines sharded blockchain and DAG blockchain. The sharded blockchain processes transactions within the building IoT, while the DAG blockchain stores block headers from the sharded network. By designing an Adaptive Balancing Load Algorithm (ABLA) for periodic network sharding, nodes are divided based on their load performance values to prevent the aggregation of low-load performance nodes and the resulting issue of shard overheating. By combining the characteristics of the KD tree and Merkle tree, a block structure known as 3D-Merkle tree is designed to support three-dimensional data queries, enhancing the efficiency of three-dimensional data queries in building IoT. By deploying and conducting simulation experiments on various physical devices, we have verified the effectiveness of the solution proposed in this paper. The results indicate that, compared to other solutions, the proposed solution is better suited for building IoT data management. ABLA is effective in preventing shard overheating issue, and the 3D-Merkle tree significantly enhances data query efficiency.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 217-234"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000014/pdfft?md5=4cbb598cfddbffa06e124be5e2862437&pid=1-s2.0-S2667345224000014-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139718720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control 智能健康学校:基于物联网的多房间动态空气质量控制概念

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2023-05-31 DOI: 10.1016/j.iotcps.2023.05.005

Alessandro Zivelonghi , Alessandro Giuseppi

{"title":"Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control","authors":"Alessandro Zivelonghi , Alessandro Giuseppi","doi":"10.1016/j.iotcps.2023.05.005","DOIUrl":"https://doi.org/10.1016/j.iotcps.2023.05.005","url":null,"abstract":"<div><p>Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO<sub>2</sub> levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO<sub>2</sub>. The multi-room monitoring concept of indoor-CO<sub>2</sub> levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 24-31"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49884564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence 基于人工智能摩擦纳米发电机的无线实时监测

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2023-09-04 DOI: 10.1016/j.iotcps.2023.08.001

Dexin Tang , Yuankai Zhou , Xin Cui , Yan Zhang

引用次数: 0

Points of interest in the city of Barcelos in Portugal through augmented reality 通过增强现实技术在葡萄牙巴塞洛斯市的景点

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2023-07-13 DOI: 10.1016/j.iotcps.2023.07.002

Miguel Pereira , João Carlos Silva , Marisa Pinheiro , Sandro Carvalho , Gilberto Santos

引用次数: 1

Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review 物联网网络入侵检测的多目标优化算法：系统综述

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2024-02-10 DOI: 10.1016/j.iotcps.2024.01.003

Shubhkirti Sharma , Vijay Kumar , Kamlesh Dutta

{"title":"Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review","authors":"Shubhkirti Sharma , Vijay Kumar , Kamlesh Dutta","doi":"10.1016/j.iotcps.2024.01.003","DOIUrl":"10.1016/j.iotcps.2024.01.003","url":null,"abstract":"<div><p>The significance of intrusion detection systems in networks has grown because of the digital revolution and increased operations. The intrusion detection method classifies the network traffic as threat or normal based on the data features. The Intrusion detection system faces a trade-off between various parameters such as detection accuracy, relevance, redundancy, false alarm rate, and other objectives. The paper presents a systematic review of intrusion detection in Internet of Things (IoT) networks using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities and reducing the chances of security attacks. MOAs provide a set of optimized solutions for the intrusion detection process in highly complex IoT networks. This paper presents the identification of multiple objectives of intrusion detection, comparative analysis of multi-objective algorithms for intrusion detection in IoT based on their approaches, and the datasets used for their evaluation. The multi-objective optimization algorithms show the encouraging potential in IoT networks to enhance multiple conflicting objectives for intrusion detection. Additionally, the current challenges and future research ideas are identified. In addition to demonstrating new advancements in intrusion detection techniques, this study attempts to identify research gaps that can be addressed while designing intrusion detection systems for IoT networks.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 258-267"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000038/pdfft?md5=19510c1690405bb751695fbd58ac122c&pid=1-s2.0-S2667345224000038-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139812070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Credit card default prediction using ML and DL techniques 利用 ML 和 DL 技术预测信用卡违约情况

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2024-09-06 DOI: 10.1016/j.iotcps.2024.09.001

Fazal Wahab , Imran Khan , Sneha Sabada

{"title":"Credit card default prediction using ML and DL techniques","authors":"Fazal Wahab , Imran Khan , Sneha Sabada","doi":"10.1016/j.iotcps.2024.09.001","DOIUrl":"10.1016/j.iotcps.2024.09.001","url":null,"abstract":"<div><p>The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 293-306"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000087/pdfft?md5=f77f275cf416221418432e3c1d730036&pid=1-s2.0-S2667345224000087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LEACH-D: A low-energy, low-delay data transmission method for industrial internet of things wireless sensors LEACH-D:一种用于工业物联网无线传感器的低能耗、低延迟数据传输方法

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2023-10-14 DOI: 10.1016/j.iotcps.2023.10.001

Desheng Liu , Chen Liang , Hongwei Mo , Xiaowei Chen , Dequan Kong , Peng Chen

{"title":"LEACH-D: A low-energy, low-delay data transmission method for industrial internet of things wireless sensors","authors":"Desheng Liu , Chen Liang , Hongwei Mo , Xiaowei Chen , Dequan Kong , Peng Chen","doi":"10.1016/j.iotcps.2023.10.001","DOIUrl":"https://doi.org/10.1016/j.iotcps.2023.10.001","url":null,"abstract":"<div><p>In recent years, the Internet of Things (IoT) has experienced extensive adoption in industrial environments, healthcare, smart cities, and more, playing a vital role in these domains. Within IoT-based systems, wireless sensor networks (WSNs) have emerged as a crucial method for collecting peripheral environmental data within industries, owing to their self-organizational attributes. Nevertheless, the enormous volume of heterogeneous data from various sensing devices presents many challenges for IoT-enabled WSNs, encompassing high transmission delay times (TD) and excessive battery energy consumption (EC). To address these challenges, it is imperative to prioritize efficiency and optimize energy utilization. Moreover, enhancing energy efficiency within the Industrial Internet of Things (IIoT) realm hinges significantly on factors such as data transmission modes and the allocation of cluster head nodes. Numerous researchers have proposed algorithms to minimize transmission time and energy consumption, specifically focusing on industrial environments. This paper introduces an inventive clustering-based data transmission algorithm for IIoT, LEACH-D, to enhance efficiency. The LEACH-D algorithm improves the transmission task duration while maintaining consistent battery energy consumption. It also seeks to elevate performance in metrics such as average transmission time during the first node death (FND). Numerous experimental results provide strong evidence that the algorithm introduced in this paper has effectively reduced the average transmission time by remarkable percentages: 51.32%, 12.12%, 12.96%, and 5.42%, while simultaneously increasing the number of FND rounds by significant margins: 222.43%, 36.63%, 33.72%, and 7.81%, respectively. These improvements stand in stark contrast to the performance of existing algorithms, including FREE_MODE, LEACH, EE-LEACH, and ETH-LEACH.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 129-137"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49883529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Internet of things enabled parking management system using long range wide area network for smart city 物联网停车管理系统采用长程广域网实现智慧城市

Internet of Things and Cyber-Physical Systems Pub Date : 2024-01-01 Epub Date: 2023-09-09 DOI: 10.1016/j.iotcps.2023.09.001

Waheb A. Jabbar , Lu Yi Tiew , Nadiah Y. Ali Shah

{"title":"Internet of things enabled parking management system using long range wide area network for smart city","authors":"Waheb A. Jabbar , Lu Yi Tiew , Nadiah Y. Ali Shah","doi":"10.1016/j.iotcps.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.iotcps.2023.09.001","url":null,"abstract":"<div><p>As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 82-98"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49884559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0