A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network
Sabri Hisham, Mokhairi Makhtar, Azwa Abdul Aziz, M Shen, X Tang, L Zhu, X Du, M Guizani, J Warraich, C Singh, P Thapa, D Boughaci, A Alkhawaldeh, Z Wang, N Luo, P Zhou, N Dhieb, H Ghazzai, H Besbes, Y Massoud, S Nakamoto, J Moubarak, E Filiol, M Chamoun, W Wang, J Song, G Xu, Y Li, H Wang, C Su, A Irwin, A Turner, L Chen, J Peng, Y Liu, J Li, F Xie, Z Zheng, M Conti, E Kumar, C Lal, S Ruj, A Khan, M Khan, K Khan, J Arshad, F Ahmad, J Liu, Z Zhao, X Cui, Z Wang, Q Liu, J Wu, Q Yuan, D Lin, W You, Chen Chen, C, M Mirtaheri, S Abu-El-Haija, F Morstatter, S Ver, A Galstyan, I Alarab, S Prakoonwit, M Nacer, S Kok, A Abdullah, N Jhanjhi, M Supramaniam, E Jung, T Le, A Gehani, Y Ge, S Iyer, S Thakur, M Dixit, R Katkam, A Agrawal, F Kazi, M Li, K Zhang, J Liu, H Gong, Z Zhang, M Bhutta, A Khwaja, A Nadeem, H Ahmad, M Khan, M Hanif, R Merkle, F Tschorsch, B Scheuermann, F Casino, T Dasaklis, C Patsakis, R Schollmeier, A Baliga, Z Zheng
Abstract
Blockchain technology has changed the global trading of assets. A blockchain can be viewed as a connected ledger managed by a distributed peer-topeer (P2P) network. Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others. These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) The increasing desire for technological advancements stimulated the development of BT.