Integrating Distributed Pattern Recognition Technique for Event Monitoring within the IoT-Blockchain Network

Hudaya, Anang; Amin, Muhamad Kamal Mohammed; Ahmad, Nazrul M.; Kannan, Subarmaniam

doi:10.1109/icias.2018.8540631

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…This paper is an extension of work originally presented in ICIAS 2018 [3]. In this paper, we extend our proposed idea on integrating a distributed pattern recognition algorithm and blockchain technology for event monitoring in IoT network.…”

Section: Introductionmentioning

confidence: 90%

Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network

Amin¹,

Ahmad²,

Kannan³

2019

Adv. sci. technol. eng. syst. j.

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With the advancement in the field of Internet-of-Things (IoT), event monitoring applications have rapidly evolved from a simple event data acquisition towards predictive event analytics involving multi-sensory data aggregation in a distributed environment. Existing event monitoring schemes are mainly relying on inefficient centralized processing mechanism, which may lead to the common single-point of failure for the entire system. In addition, there is no proper method for verifying the event data generated by the monitoring system. In this paper, we present a distributed event monitoring scheme using a Hierarchical Graph Neuron (HGN) distributed pattern recognition algorithm. HGN is a single-cycle learning graph-based recognition scheme that is modelled for in-network deployment. In this work, event data retrieved from multi-sensory IoT devices within a distributed event monitoring network is converted into pattern. To address the event data verification problem, we integrate our proposed scheme with blockchain technology. Combining this IoT event monitoring capabilities with blockchain-based data storage and verification could leads towards a scalable event detection and monitoring model for large-scale network. The results obtained from our simulation shows that the proposed scheme offers high event detection accuracy and capable of minimizing the event storage requirements on blockchain network.

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Section: Introductionmentioning

confidence: 90%

Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network

Amin¹,

Ahmad²,

Kannan³

2019

Adv. sci. technol. eng. syst. j.

Self Cite

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“…Plenty of similar integration solutions were proposed to enhance stand-alone ML or BC systems. Objectives for integrating ML and BC may include intrusion detection [31,32,33,34], monitoring [35,36,37], optimization [38,39,40], price prediction [41,42,43,44], and security [45,46,47,48,49]. However, few previous works investigated the utilization of ML to optimize the classical mining (i.e.…”

Section: B Blockchain and Machine Learningmentioning

confidence: 99%

Machine Learning for Alternative Mining in PoW-based Blockchains: Theory, Implications and Applications

Baniata¹,

Prodan²,

Kertész³

2022

Preprint

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<p>Blockchain (BC) technology provides a secure distributed transactional database, that can enhance the security and privacy of decentralized systems and applications, e.g. distributed identity, supply chain and Internet of Things (IoT). The most known secure consensus mechanism for permissionless BCs is the Proof-of-Wok (PoW) algorithm. Many argue that the fastest approach to mine new blocks in PoW-based BC networks (although too energy consuming) is Brute-forcing the nonce. In this paper, we demonstrate how a well-trained Machine Learning (ML) model can find more accurate initial nonce values for this problem aiming to decrease the total energy consumption. We attempt to identify linear relationships between inputs and outputs of the classical mining processes, which typically use pure Brute-force techniques to solve the problem. Then, we integrate two ML models, namely SGDRegressor and LinearRegressor with PolynomialFeatures, into a classical mining method to predict the solution. For this, we use more than 780k+ real Bitcoin blocks for training and testing. We mathematically formalize our analysis and propose equations to predict and score the total enhancement, for any ML model deployment, compared to classical mining. We experimentally prove that our method can mine faster than the classical method. Furthermore, we discuss the implications on the node level and the network level, including the allowance for taking over the system by controlling a portion of only 35.5% out of the total computational power of the network. Finally, we apply our method in an integrated IoT-Fog-Blockchain system to enhance the fairness among participating miners.</p>

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“…In this dimension, research work has been included from the following aspects: (i) preventing, (ii) predicting, (iii) monitoring, (iv) detection, and (v) response of blockchain. For instance, [29] discussed the detection of intrusion in a collaborative manner, [30] predicts bitcoin prices using Bayesian regression method and [31] monitors the blockchain network in a distributed manner.…”

Section: Goal Orientedmentioning

confidence: 99%

“…The result shows the effective key attributes monitoring of the blockchain nodes and finds the emerging patterns. More, another approach included the distributed pattern recognition system with the concept of Graph Neurons (GNs) [31] to monitor the blockchain system. GNs are scalable and could recognize patterns from similar or incomplete patterns.…”

Section: B Monitoringmentioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion. INDEX TERMS Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

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Integrating Distributed Pattern Recognition Technique for Event Monitoring within the IoT-Blockchain Network

Cited by 5 publications

References 9 publications

Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network

Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network

Machine Learning for Alternative Mining in PoW-based Blockchains: Theory, Implications and Applications

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

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