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Browsing by Author "Ozsoy, Adnan"

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    Article
    Citation - WoS: 20
    Citation - Scopus: 29
    Creating consensus group using online learning based reputation in blockchain networks
    (Elsevier, 2019) Bugday, Ahmet; Ozsoy, Adnan; Oztaner, Serdar Murat; Sever, Hayri; 11916; Bilgisayar Mühendisliği
    One of the biggest challenges to blockchain technology is the scalability problem. The choice of consensus algorithm is critical to the practical solution of the scalability problem. To increase scalability, Byzantine Fault Tolerance (BFT) based methods have been most widely applied. This study proposes a new model instead of Proof of Work (PoW) for forming the consensus group that allows the use of BFT based methods in the public blockchain network. The proposed model uses the adaptive hedge method, which is a decision-theoretic online learning algorithm (Qi et al., 2016). The reputation value is calculated for the nodes that want to participate in the consensus committee, and nodes with high reputation values are selected for the consensus committee to reduce the chances of the nodes in the consensus committee being harmful. Since the study focuses on the formation of the consensus group, a simulated blockchain network is used to test the proposed model more effectively. Test results indicate that the proposed model, which is a new approach in the literature making use of machine learning for the construction of consensus committee, successfully selects the node with the higher reputation for the consensus group. (C) 2019 Elsevier B.V. All rights reserved.
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    Citation - WoS: 0
    Citation - Scopus: 13
    Securing blockchain shards by using learning based reputation and verifiable random functions
    (Ieee, 2019) Bugday, Ahmet; Ozsoy, Adnan; Sever, Hayri; 11916; Bilgisayar Mühendisliği
    In order to meet the increasing demand of the blockchain, it needs to find a solution to the scalability problem. It has been focused on sharding recently to address the scalability problem. In the sharding method, the blockchain is divided into pieces. Instead of a more extensive network, networks with fewer nodes are created. As a result, it becomes more important that each node in the network is reliable. In this study, studies using sharding method have been investigated, and methods for the assigning nodes to shards are proposed. The use of learning-based adaptive methods for this process will contribute to the safe and reliable use of shards. The probability of the shards to deteriorate and influence the whole blockchain will be reduced.