Management and Monitoring of IoT Devices Using Blockchain

Košťál, Kristián; Helebrandt, Pavol; Belluš, Matej; Ries, Michal; Kotuliak, Ivan

doi:10.3390/s19040856

Cited by 105 publications

(49 citation statements)

References 25 publications

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“…It combines the SaaS business model with blockchain, which was used to buy and sell sensor data. Reference [11] proposed an improved blockchain structure. In the enterprise network, the private chain was used to manage the device configuration files of IoT in a distributed way.…”

Section: Related Workmentioning

confidence: 99%

Fabric-iot: A Blockchain-Based Access Control System in IoT

2020

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IoT devices have some special characteristics, such as mobility, limited performance, and distributed deployment, which makes it difficult for traditional centralized access control methods to support access control in current large-scale IoT environment. To address these challenges, this paper proposes an access control system in IoT named fabric-iot, which is based on Hyperledger Fabric blockchain framework and attributed based access control (ABAC). The system contains three kinds of smart contracts, which are Device Contract (DC), Policy Contract (PC), and Access Contract (AC). DC provides a method to store the URL of resource data produced by devices, and a method to query it. PC provides functions to manage ABAC policies for admin users. AC is the core program to implement an access control method for normal users. Combined with ABAC and blockchain technology, fabric-iot can provide decentralized, fine-grained and dynamic access control management in IoT. To verify the performance of this system, two groups of simulation experiments are designed. The results show that fabric-iot can maintain high throughput in largescale request environment and reach consensus efficiently in a distributed system to ensure data consistency.

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Section: Related Workmentioning

confidence: 99%

Fabric-iot: A Blockchain-Based Access Control System in IoT

2020

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“…Ethereum [47] and Hyperledger Fabric [48] are the two most popular blockchain application development platforms. Their underlying technologies are the same.…”

Section: Blockchainmentioning

confidence: 99%

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

et al. 2020

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With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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“…DLP likes Intrusion Detection System (IDS) in identifying malicious act and notifying person who in charge of network. On other hands, IDS is not designed to avoid data leakage [23]. Finally, network monitor of DLP can be deployed near router or switch that connected all devices together to control all incoming and outcoming packets [24].…”

Section: Data In Motionmentioning

confidence: 99%

Data loss prevention (DLP) by using MRSH-v2 algorithm

Ali

Jalal

Al-Obaydy

2020

IJECE

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Sensitive data may be stored in different forms. Not only legal owners but also malicious people are interesting of getting sensitive data. Exposing valuable data to others leads to severe Consequences. Customers, organizations, and /or companies lose their money and reputation due to data breaches. There are many reasons for data leakages. Internal threats such as human mistakes and external threats such as DDoS attacks are two main reasons for data loss. In general, data may be categorized based into three kinds: data in use, data at rest, and data in motion. Data Loss Prevention (DLP) are good tools to identify important data. DLP can do analysis for data content and send feedback to administrators to make decision such as filtering, deleting, or encryption. Data Loss Prevention (DLP) tools are not a final solution for data breaches, but they consider good security tools to eliminate malicious activities and protect sensitive information. There are many kinds of DLP techniques, and approximation matching is one of them. Mrsh-v2 is one type of approximation matching. It is implemented and evaluated by using TS dataset and confusion matrix. Finally, Mrsh-v2 has high score of true positive and sensitivity, and it has low score of false negative.

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Management and Monitoring of IoT Devices Using Blockchain

Cited by 105 publications

References 25 publications

Fabric-iot: A Blockchain-Based Access Control System in IoT

Fabric-iot: A Blockchain-Based Access Control System in IoT

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

Data loss prevention (DLP) by using MRSH-v2 algorithm

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