A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection

Podgorelec, Blaž; Turkanović, Muhamed; Karakatič, Sašo

doi:10.3390/s20010147

Cited by 56 publications

(26 citation statements)

References 22 publications

(22 reference statements)

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“…Thanks to the recent advancement in Blockchain, IoT, and social media technologies [20] , [50] , [60] , [100] , [101] , we can develop such a health service [107] . Written transactions on blockchain and off-chain can be intelligently trained by deep learning applications [102] . We used the Paillier algorithm for additive operations, and RSA for multiplicative homomorphic operations and AES-256 symmetric key encryption algorithm in our FL architecture [87] .…”

Section: System Designmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

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Section: System Designmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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“…Podgorelec et al [ 81 ] focussed on frauds in blockchain transactions by introducing ML-based signing and information monitoring. To this end, they applied Isolation Forest, an unsupervised anomaly detection method.…”

Section: Fraud Detection In the Fintech Domainmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.

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“…Solution Approach Attack DU DC S. Iyer [24] storage off-chain S. Sayadi [25] storage on-chain Y. Mirsky [26] storage off-chain M. Li [27] storage off-chain O. Alkadi [28] storage off-chain S. Morishima [29] other off-chain Z. Il-Agure [30] other off-chain M. Salimitari [31] framework off-chain X. Wang [32] framework on-chain B. Podgorelec [33] framework on-chain BAD (Our solution) framework on-chain…”

Section: Ads Challengesmentioning

confidence: 99%

BAD: A Blockchain Anomaly Detection Solution

et al. 2020

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Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks specifically targeting blockchain-based systems. In this paper we present BAD: Blockchain Anomaly Detection. This is the first solution, to the best of our knowledge, that is tailored to detect anomalies in blockchain-based systems. BAD is a complete framework, relying on several components leveraging, at its core, blockchain meta-data in order to collect potentially malicious activities. BAD enjoys some unique features: (i) it is distributed (thus avoiding any central point of failure); (ii) it is tamper-proof (making it impossible for a malicious software to remove or to alter its own traces); (iii) it is trusted (any behavioral data is collected and verified by the majority of the network); and, (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is described in detail and validated via both experimental results and analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.

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A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection

Cited by 56 publications

References 22 publications

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

BAD: A Blockchain Anomaly Detection Solution

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