Internet of Things最新文献

{"title":"IoTSLE: Securing IoT systems in low-light environments through finite automata, deep learning and DNA computing based image steganographic model","authors":"Subhadip Mukherjee,&nbsp;Somnath Mukhopadhyay,&nbsp;Sunita Sarkar","doi":"10.1016/j.iot.2024.101358","DOIUrl":"10.1016/j.iot.2024.101358","url":null,"abstract":"<div><p>The Internet of Things (IoT) is a vast network of interconnected devices and systems, including wearables, smart home appliances, industrial machinery, and vehicles, equipped with sensors and connectivity. The data collected by the IoT devices are transmitted over a network, for processing and analyzing that data, so that appropriate actions can be initiated. Security of IoT systems is a major concern, as IoT devices collect and transmit crucial information. But images captured in low-light environments pose a challenge for IoT security by limiting the ability to accurately identify objects and people, increasing the risk of spoofing, and hindering forensic analysis. This paper unfolds a novel framework for IoT security using Steganography in Low-light Environment (IoTSLE) by image enhancement and data concealment. In proposed IoTSLE, initially, the low-light images, captured by the IoT devices in a low-light environment, are enhanced by band learning with recursion and band recomposition. After that, the secret information is concealed within the enhanced image. This concealment is supervised by using a specially designed finite automata for genome sequence encoding and 2-2-2 embedding. The proposed steganography technique is capable of hiding secret information within a 512 × 512 RGB image with the payload of 2<!--> <!-->097<!--> <!-->152 bits. The experiments like, PSNR, SSIM, Q-Index, BER, NCC, and NAE etc. are conducted to analyze the imperceptibility and security of IoTSLE. The proposed IoTSLE is useful for various IoT systems in different private and government fields like, defense agencies, digital forensics, agriculture, healthcare industry, cybersecurity firms, smart home, smart city etc.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101358"},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimized and intelligent metaverse intrusion detection system based on rough sets","authors":"Gehad Ismail Sayed ,&nbsp;Aboul Ella Hassanien","doi":"10.1016/j.iot.2024.101360","DOIUrl":"10.1016/j.iot.2024.101360","url":null,"abstract":"<div><p>The convergence of the Internet of Things (IoT) and the Metaverse is revolutionizing the digital world by providing immersive, interactive environments as well as new data transmission opportunities. However, this rapid integration raises complex security issues, including an increased risk of unlawful access and data breaches. Strong cybersecurity measures are required to identify and prevent these attacks, preserving the security and confidentiality of users. Finding significant features for recognizing malicious attacks and enhancing the accuracy of network intrusion detection in general, and particularly in the virtual environment, are some of the significant research needs. This paper introduces an intelligent intrusion detection system (IIDS) based on rough set-based electric eel foraging optimization (RSEEFO) in conjunction with the AdaBoost-based classification algorithm. The main objective of this system is to detect and recognize different types of attacks on the interaction and connectivity between the IoT and the metaverse. The proposed IIDS-RSEEFO consists of three phases, which are data pre-processing, multi-IoT attack classification, and evaluation. The main problems associated with the adopted dataset are handled in data pre-processing. Then, in the second phase, a one-versus-all approach is employed along with RSEEFO and AdaBoost to handle the multi-class classification problem. Finally, several evaluation metrics are employed to assess the reliability and robustness of the proposed IIDS-RSEEFO. The proposed IIDS was tested on CIC-IoT-2023 and validated on UNSWNB-15. It achieved high accuracy across all attack types of CIC-IoT-2023, with accuracies of 99.7 %, 100 %, 100 %, 99.8 %, 100 %, 100 %, 99.8 %, and 100 % for benign traffic, DDoS, brute force, spoofing, DoS, Mirai, recon, and web-Based respectively, accompanied by robust sensitivity, F1-Score, Specificity, G-Mean, and crossover-error rate metrics demonstrating its effectiveness in accurately predicting each attack type. Additionally, it obtained high accuracy for all attack types of UNSWNB-15, with accuracies of 96.48 %, 99.12 %, 99.24 %, 93.78 %, 92.55 %, 92.55 %, 92.55 %, 94.73 %, 94.73 %, 98.09 %, 98.39 %, 99.50 %, and 99.95 % for analysis, backdoor, DoS, exploits, fuzzers, generic, normal, reconnaissance, shellcode, and worms, respectively. In addition, the results evaluated that the proposed model is superior compared to the existing intrusion detection systems.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101360"},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time series processing-based malicious activity detection in SCADA systems","authors":"Michael Zaslavski,&nbsp;Meir Kalech","doi":"10.1016/j.iot.2024.101355","DOIUrl":"10.1016/j.iot.2024.101355","url":null,"abstract":"<div><p>Many critical infrastructures, essential to modern life, such as oil and gas pipeline control and electricity distribution, are managed by SCADA systems. In the contemporary landscape, these systems are interconnected to the internet, rendering them vulnerable to numerous cyber-attacks. Consequently, ensuring SCADA security has become a crucial area of research. This paper focuses on detecting attacks that manipulate the timing of commands within the system, while maintaining their original order and content. To address this challenge, we propose several machine-learning-based methods. The first approach relies on Long-Short-Term Memory model, and the second utilizes Hierarchical Temporal Memory model, both renowned for their effectiveness in detecting patterns in time-series data. We rigorously evaluate our methods using a real-life SCADA system dataset and show that they outperform previous techniques designed to combat such attacks.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101355"},"PeriodicalIF":6.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive IoT edge based smart irrigation system for tomato cultivation","authors":"Rohit Kumar Kasera,&nbsp;Tapodhir Acharjee","doi":"10.1016/j.iot.2024.101356","DOIUrl":"10.1016/j.iot.2024.101356","url":null,"abstract":"<div><p>Agriculture industry is the primary engine for a country's economic development. Growing crops using minimum irrigation water is a major challenge for farmers. In conventional farming, crops may be affected by various diseases due to inadequate irrigation scheduling. Recent proposals have suggested using Edge-IoT, AI, and distributed computing to accelerate the inference procedure utilized in smart irrigation applications. The use of resource-constrained edge servers and edge devices used to deliver smart agriculture applications can cause latency-sensitive workloads to interfere with one another. To address this issue, we design a long-range (LoRa) edge IoT computing-based sustainable and customized smart irrigation framework to capture the real-time data of tomato plants. This helps in automatic underground drip irrigation scheduling. This also predicts total water demand and usage, and measure plant growth status. The edge-IoT cloud data transmission control and optimization has been enforced using Smart irrigation data optimization and robust transmission (SIDORT) Message Queuing Telemetry Transport (MQTT) system. We develop a hybrid algorithm named Linked least traversal (LLT) for machine-to-machine communication (M2M). Also, a Reinforcement learning (RL) based Optimal Soil Wetness Closeness Policy (OSWCP) for irrigation scheduling has been proposed. The performance of the proposed smart irrigation models has been validated through extensive experiments using real-time data in which OSWCP performance has been measured at a 97.88 % accuracy rate. Additionally, a comparison of our proposed architecture has been accomplished by resolving the existing smart irrigation system challenges.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101356"},"PeriodicalIF":6.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoTDeploy: Deployment of IoT Smart Applications over the Computing Continuum","authors":"Francis Borges Oliveira ,&nbsp;Marco Di Felice ,&nbsp;Carlos Kamienski","doi":"10.1016/j.iot.2024.101348","DOIUrl":"10.1016/j.iot.2024.101348","url":null,"abstract":"<div><p>IoT Smart Applications have created a demand for architectures, infrastructure, platforms, orchestration, and service deployment strategies. They are deployed from sensors to the cloud over a geographical and computing continuum, which is challenging for service orchestration and DevOps strategies. The distributed infrastructure to implement the end-to-end data path may vary, even for similar applications, concerning the services deployed over the mist, fog, edge, and cloud stages. This paper proposes and evaluates IoTDeploy, a solution for streamlining and scaling static and dynamic IoT service deployment over the continuum. IoTDeploy implements a CI/CD tool plugin for deploying applications running on the continuum and supports dynamic service migration. We evaluated the service migration from cloud to fog and fog to cloud with a case study on smart irrigation in agriculture. The experiments reveal that deployment in IoT-distributed environments is reliable and resilient, enabling migration without interrupting the application and losing data.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101348"},"PeriodicalIF":6.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positive connotations of map-matching based on sub-city districts for trajectory data analytics","authors":"Zheng-Yun Zhuang ,&nbsp;Ye Ding","doi":"10.1016/j.iot.2024.101338","DOIUrl":"10.1016/j.iot.2024.101338","url":null,"abstract":"<div><div>We propose a new data pre-processing method, sub-district-based map matching (SDBMM), which involves projecting a trajectory onto sub-city districts (SCDs), geographical areas on the map with irregular boundaries. Thus, this method differs significantly from traditional map-matching processes, which match trajectory data points to the nearest or most probable road segments. With SDBMM, a moving object is represented through gradual transitions through a set of SCDs instead of ‘blinking’ at the road segments rapidly. This change in information granularity yields more presentation efficiency. As SCDs are meaningful partitions of a city based on urban planning or travel behaviours, SDBMM also underlies a new ground for post hoc analytics. In the first application, we perform SDBMM for a large taxi-trajectory dataset in a large city. This case verifies SDBMM with sufficient massive data and brings valuable knowledge to several parties (i.e. taxi drivers, service operators, and the government) in managing the taxi transport mode in the city and policy-making. We apply SDBMM in a second application of anti-drug investigation and find that SCDs with pathway entrances/exits across the mountain ranges are usually the hot traces of drug transactions. These practical applications may foster greater confidence in future utilisations of SDBMM.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101338"},"PeriodicalIF":6.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT","authors":"Sajjad Dadkhah,&nbsp;Euclides Carlos Pinto Neto,&nbsp;Raphael Ferreira,&nbsp;Reginald Chukwuka Molokwu,&nbsp;Somayeh Sadeghi,&nbsp;Ali A. Ghorbani","doi":"10.1016/j.iot.2024.101351","DOIUrl":"10.1016/j.iot.2024.101351","url":null,"abstract":"<div><p>The Internet of Things (IoT) is increasingly integrated into daily life, particularly in healthcare, through the Internet of Medical Things (IoMT). IoMT devices support services like continuous health monitoring but raise significant cybersecurity concerns due to their vulnerability to various attacks. The complexity and data volume of IoMT network traffic requires advanced methods to enhance security and reliability. Machine Learning (ML) offers techniques to detect, prevent, and mitigate cyberattacks. However, existing benchmark datasets lack essential features for robust IoMT security solutions, such as a reduced number of real devices, a limited variety of attacks, and a lack of extensive profiling. We propose a realistic benchmark dataset for IoMT security solutions development and evaluation to address these gaps. We executed 18 attacks on an IoMT testbed with 40 devices (25 real and 15 simulated), using protocols like Wi-Fi, MQTT, and Bluetooth. Supporting technologies, including dedicated network traffic collectors and a Faraday Cage, ensured data quality. The attacks fall into five categories: DDoS, DoS, Recon, MQTT, and spoofing. We aim to establish a baseline that complements existing datasets, aiding researchers in creating secure healthcare systems using ML. Beyond simulating attacks, we capture the lifecycle of IoMT devices from network entry to exit through profiling, allowing classifiers to identify device anomalies. The resulting <span><span>CICIoMT2024</span><svg><path></path></svg></span> dataset, published on the CIC dataset page, demonstrates that various methods can classify IoMT cyberattacks. This effort supports new IoMT security solutions and contributes to the broader field of cybersecurity in healthcare, ensuring more reliable IoMT device deployment.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101351"},"PeriodicalIF":6.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002920/pdfft?md5=0430302d374a16cfc8032840ccdad749&pid=1-s2.0-S2542660524002920-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy","authors":"Xiuping Kong ,&nbsp;Lin Lu ,&nbsp;Ke Xiong","doi":"10.1016/j.iot.2024.101344","DOIUrl":"10.1016/j.iot.2024.101344","url":null,"abstract":"<div><p>With the popularity of connected electric vehicles, the openness and sharing of charging data between stakeholders allows a more accurate estimation of charging behavior, which is valuable for optimizing energy systems and facilitating travel convenience. However, to enable such an effective mechanism, the challenge of data security and privacy should be addressed. Federated learning in the vehicular network is appealing for utilizing individual vehicle data while preserving data privacy. We propose an improved local differential privacy-based federated learning approach for modeling charging session prediction problems while preserving user privacy against the threat from a honest-but-curious server. In this approach, all vehicles, within the coordination of a cloud server, collaboratively establish a global regression network through parameter exchange. Meanwhile, the servers may belong to third-party model owners and can be semi-honest when inferring private information on the collected model parameters. Hence, local differential privacy is adopted to perturb the parameters. Additionally, a combination of local and global models via elastic synchronization is proposed to improve the accuracy of the learned noisy global model. Through the test on a real data set, the results show the superiority of the proposed algorithm over traditional noisy federated learning methods. Furthermore, the practical value of the proposed method is validated with a real-world charging case. Such an accurate charging session prediction service for electric vehicle drivers facilitates charging and travel convenience in the green transportation world.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101344"},"PeriodicalIF":6.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel deep learning-based intrusion detection system for IoT DDoS security","authors":"Selman Hizal ,&nbsp;Unal Cavusoglu ,&nbsp;Devrim Akgun","doi":"10.1016/j.iot.2024.101336","DOIUrl":"10.1016/j.iot.2024.101336","url":null,"abstract":"<div><p>Intrusion detection systems (IDS) for IoT devices are critical for protecting against a wide range of possible attacks when dealing with Distributed Denial of Service (DDoS) attacks. These attacks have become a primary concern for IoT networks. Intelligent decision-making techniques are required for DDoS attacks, which pose serious threats. The range of devices connected to the IoT ecosystem is growing, and the data traffic they generate is continually changing; the need for models more resistant to new attack types and existing attacks is of research interest. Motivated by this gap, this paper provides an effective IDS powered by deep learning models for IoT networks based on the recently published CICIoT2023 dataset. In this work, we improved the detection and mitigation of potential security threats in IoT networks. To increase performance, we performed preprocessing operations on the dataset, such as random subset selection, feature elimination, duplication removal, and normalization. A two-level IDS using deep-learning models containing binary and multiclass classifiers has been designed to identify DDoS attacks in IoT networks. The effectiveness of several deep-learning models in real-time and detection performance has been evaluated. We trained fully connected, convolutional, and LSTM-based deep learning models for detecting DDoS attacks and sub-classes. According to the results on a partially balanced sub-dataset, two staged models performed better than baseline models such as DNN (Deep Neural Networks), CNN (Convolutional Neural Networks), LSTM (Long Short Term Memory), RNN (Recurrent Neural Network).</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101336"},"PeriodicalIF":6.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data fusion integrated network forecasting scheme classifier (DFI-NFSC) via multi-layer perceptron decomposition architecture","authors":"Erdem Çakan ,&nbsp;Volkan Rodoplu ,&nbsp;Cüneyt Güzeliş","doi":"10.1016/j.iot.2024.101341","DOIUrl":"10.1016/j.iot.2024.101341","url":null,"abstract":"<div><p>The Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA, LSTM, and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway’s coverage zone.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101341"},"PeriodicalIF":6.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}