Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage

Ataei, Mohammadhossein; Eghmazi, Ali; Shakerian, Ali; Landry, Rene; Chevrette, Guy

doi:10.3390/s23249692

Cited by 2 publications

(1 citation statement)

References 21 publications

(25 reference statements)

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“…Regarding synchronization decoupling, when publishing or receiving events occur, there is no need to block endpoint operations. Consequently, the publish-subscribe model operates in real-time communication, leading to increased scalability and reliability [13][14][15].…”

Section: The Operation Strategy Of the Mqtt Protocol And Its Attacksmentioning

confidence: 99%

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Alotaibi,

Sayed Ahmed,

Kamel

et al. 2024

Sensors

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The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O’s algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques.

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Section: The Operation Strategy Of the Mqtt Protocol And Its Attacksmentioning

confidence: 99%

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Alotaibi,

Sayed Ahmed,

Kamel

et al. 2024

Sensors

View full text Add to dashboard Cite

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A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model

Idrees,

Maiti

2024

Applied Sciences

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The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data.

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Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage

Cited by 2 publications

References 21 publications

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model

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