International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume4,December 2017,

Topic : Anomaly Detection in Blockchain Using Clustering Protocol

Authors:Rachana Kumari || Monica Catherine

Abstract:With the growth of technology, everyday new technologies are evolving. Blockchain is one such technology. Blockchain is a revolutionary technology which has proved its potential of being used in numerous fields like digital money or crypto-currency, IoT, Product tracing, Smart Contracts etc. Blockchain is a distributed database which allows to share or process data between multiple parties over a network of non-trusted users securely. One of the biggest advantages of blockchain is that it is fully decentralized i.e. there is no central authority which is governing it. However, it has its own set of disadvantages also. Some of these problems are its complexity, network size, large energy consumption etc. But one key problem is that there is no way to find the anomalous node i.e. nodes which are malicious. In any Blockchain network, there are two types of nodes, the one which behaves normally i.e. the honest node. But some nodes may try to cheat in the network or may have some illegal interest I.e. malicious nodes. If someone tries to monitor this node behaviour manually, it will take tremendous time and effort and is nearly impossible. So, this paper introduces a novel solution for the above mentioned problem. The problem can be solved by clustering the nodes of the network. For this, we will propose an algorithm which will help us in clustering the blockchain and then further for analysing the malicious activity of the nodes, if any performed. In this, we will divide the whole network into clusters of nodes or data points depending or based on some similar traits that they may have. Therefore the aim of our project is to segregate groups having similar traits from the blockchain network and then cluster them so that to identify the malicious node or illegal behaviour..

Keywords: Blockchain, Cluster, Clustering Algorithm, K-means clustering, Security.

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DOI: 01.1617/vol4/iss12/pid74306

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Dr. Allon Guez
Professor, Drexel University,

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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