Anomaly Detection Model Over Blockchain Electronic Transactions

Sayadi, Sirine; Rejeb, Sonia Ben; Choukair, Zièd

doi:10.1109/iwcmc.2019.8766765

Cited by 63 publications

(18 citation statements)

References 4 publications

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“…The research by Sayadi et al [15] proposes an algorithm for anomaly detection over bitcoin electronic transactions. They examined the One-Class Support Vector Machines (OCSVM) and the K-means algorithms to group outliers similar in both statistical significance and type.…”

Section: Related Workmentioning

confidence: 99%

Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

Yazdinejad,

Dehghantanha,

Parizi

et al. 2022

Preprint

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Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this paper, we use Federated Learning (FL) to build a threat hunting framework called Block Hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block Hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, Block Hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the Block Hunter in detecting anomalous activities with high accuracy and minimum required bandwidth.

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Section: Related Workmentioning

confidence: 99%

Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

Yazdinejad,

Dehghantanha,

Parizi

et al. 2022

Preprint

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“…Health-care industry comprises medical records, images, documents and lab reports, which require a significant amount of storage space. With the increase in the number of patients, there will be insufficient space for the data storage (Alonso et al , 2019; Girardi et al , 2020; Margheri et al , 2020; Sayadi et al , 2019; Shamshad et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Barriers to blockchain adoption in health-care industry: an Indian perspective

Sharma

Joshi

2021

JGOSS

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Purpose This paper aims to identify barriers toward the adoption of blockchain (BC) technology in Indian health-care industry and also examines the significant issues of BC applications in health-care industry. Design/methodology/approach The barriers of the study are identified by two phases including the review of literature and semistructured interviews with hospital staff and administration operating in India. The experts (N = 15) are being taken from top-level management, IT experts and patients from the hospitals. The study implemented integrated total interpretative structural modeling-FUZZY-Cross-impact matrix multiplication applied to classification (TISM-FUZZY-MICMAC) methods for identifying the interrelationship among the barriers. Findings A total of 15 barriers have been determined in the Indian health-care industry through discussion with the selected experts. TISM is applied to develop multilevel structure for BC barriers. Further, FUZZY-MICMAC has been used to compute driving and dependent barriers. The findings suggest that low awareness related to legal issues and low support from high level of management have maximum driving power. Research limitations/implications The present study applies multicriterion approach to identify the limited barriers in BC adoption in health care. Future studies may develop the relationship and mark down the steps for implementation of BC in health-care setting of a developing economy. Empirical study can be conducted to verify the results along with selected case studies. Practical implications The present study identifies the BC adoption barriers in health-care industry. The study examines the pertinent issues in context to major support required, bottlenecks in adoption, key benefits of adoption planning and activities. The technology adoption practices are expected to provide applications such as distributed, secured medical and clinical data and patient centric systems that will enhance the efficiency of the health-care industry. Originality/value The study is among few primary studies that identify and analyze the BC adoption in health-care industry.

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“…As cryptocurrency threats and malicious user activities are driven by various factors and trends, 14 machine learning and network representational learning techniques have evolved as a remarkable mechanisms for blockchain transactions fraud detection. 15 The predictive power of feature learning through graph representation and machine learning from blockchain networks can be leveraged for multiple purposes, including, but not limited to, cryptocurrency price forecasting (eg, and malignant transactions' detection (eg, . The work presented in this paper builds upon state-of-art efforts to leverage network representation learning-based feature extraction from blockchain dataset graphs as a baseline to carry out machine learning-based detection of malicious user and transaction activities.…”

Section: Related Workmentioning

confidence: 99%

Anomaly detection in blockchain using network representation and machine learning

Martin

Rahouti

Ayyash

et al. 2021

Security and Privacy

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The vast majority of digital currency transactions rely on a blockchain framework to ensure quick and accurate execution. As such, understanding how a blockchain works is vital to understanding the dynamics of cryptocurrency operations. One of the key benefits of this type of system is the exhaustive records captured in a given marketplace. The interwoven movement between agents can effectively be expressed as a graph via the extraction of historical data from the blockchain. By looking at a specific blockchain as an interaction of its agents, network representation learning can be leveraged to examine these relationships. Furthermore, the analysis of a graph structure can be enhanced through the application of modern and sophisticated machine learning techniques. Leveraging the automated nature of these methods can create meaningful observations of the input network. In this paper, we utilize several machine learning models to detect anomalous transactions in various digital currency markets. We find that supervised learning techniques yield encouraging results, whereas unsupervised learning techniques struggle more with the classification.

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Anomaly Detection Model Over Blockchain Electronic Transactions

Cited by 63 publications

References 4 publications

Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

Barriers to blockchain adoption in health-care industry: an Indian perspective

Anomaly detection in blockchain using network representation and machine learning

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