Abstract
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.
Highlights
From the past few decades, data has become an essential source of intelligence and carries new opportunities to the real-life problems such as wireless communications, bioinformatics [1], agriculture [2], and finance [3] through smart applications
Some of the properties are: (i) Security: Nodes that do not control rare resources majorly cannot convince others for a different version of the ledger. (ii) Liveness: Here, new blocks can be added to the blockchain with suitable latency. (iii) Stability: Nodes within the blockchain network should not amend their belief of the consensus ledger except rare cases. (iv) Accuracy: Blocks added to the ledger must signify valid transactions such as they imitate to a description of how new blocks relate to previous blocks
The distributed ledger has the possibility to work as the backbone of various smart applications such as smart cities, Unmanned Aerial Vehicle (UAV), Smart Grid (SG), data trading
Summary
From the past few decades, data has become an essential source of intelligence and carries new opportunities to the real-life problems such as wireless communications, bioinformatics [1], agriculture [2], and finance [3] through smart applications. In a communication network of blockchain-based smart applications, there is layer-wise handling of security issues. The attacks are becoming more sophisticated with unseen patterns to bypass a firewall To prevent this issue, packets header data can be analyzed using ML models [8] in real-time using historical data. Packets header data can be analyzed using ML models [8] in real-time using historical data This analysis helps to detect new and changing patterns. Several blockchain-based smart applications such as UAV [9], Data Trading [10], SG build trust between data exchangers [11]. In each part of the taxonomy, existing work has been discussed in detailed to handle several issues, such as preventing and predicting attacks on the blockchain network. A case study is presented to demonstrate the usage of ML techniques in blockchain-based smart applications, such as SG, UAV, etc
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