References
[1] D. Marbouh, T. Abbasi, F. Maasmi, I. Omar, M. Debe, K. Salahet al., "Blockchain for covid-19: review, opportunities, and a trusted tracking system", Arabian Journal for Science and Engineering, vol. 45, no. 12, p. 9895-9911, https://doi.org/10.1007/s13369-020-04950-4 [2] A. Cetinkaya, H. Ishii, & T. Hayakawa, "An overview on denial-of-service attacks in control systems: attack models and security analyses", Entropy, vol. 21, no. 2, p. 210, https://doi.org/10.3390/e21020210 [3] K. Albulayhi, A. Smadi, F. Sheldon, & R. Abercrombie, "Iot intrusion detection taxonomy, reference architecture, and analyses", Sensors, vol. 21, no. 19, p. 6432, 2021. https://doi.org/10.3390/s21196432 [4] L. Huang, G. Jia, W. Fang, W. Chen, & W. Zhang, "Towards security joint trust and game theory for maximizing utility: challenges and countermeasures", Sensors, vol. 20, no. 1, p. 221, 2019. https://doi.org/10.3390/s20010221 [5] B. Kim, K. Kim, B. Shah, F. Chow, & K. Kim, "Wireless sensor networks for big data systems", Sensors, vol. 19, no. 7, p. 1565, 2019. https://doi.org/10.3390/s19071565 [6] S. Shi, D. He, L. Li, N. Kumar, M. Khan, & K. Choo, "Applications of blockchain in ensuring the security and privacy of electronic health record systems: a survey", Computers & Security, vol. 97, p. 101966, https://doi.org/10.1016/j.cose.2020.101966 [7] R. Asif, K. Ghanem, & J. Irvine, "Proof-of-puf enabled blockchain: concurrent data and device security for internet-of-energy", Sensors, vol. 21, no. 1, p. 28, 2020. https://doi.org/10.3390/s21010028 [8] W. She, Q. Liu, T. Zhao, J. Chen, B. Wang, & W. Liu, "Blockchain trust model for malicious node detection in wireless sensor networks", Ieee Access, vol. 7, p. 38947- 38956, 2019. https://doi.org/10.1109/access.2019.2902811 [9] W. Li, Y. Wang, W. Meng, L. Jin, & C. Su, "Blockcsdn: towards blockchain-based collaborative intrusion detection in software defined networking", Ieice Transactions on Information and Systems, vol. E105.D, no. 2, p. 272-279, 2022. https://doi.org/10.1587/transinf.2021bcp0013 [10]. R. F. Mansour Artificial intelligence based optimization with deep learning model for blockchain enabled intrusion detection in CPS environment. Scientific Reports, vol 12, no. 1, p.12937, 2022. [11]. P.S. Ezekiel, O.E. Taylor & F.B. Deedam-Okuchaba. A model to detect phishing websites using support vector classifier and a deep neural network algorithm. International Journal of Advanced Research in Computer and Communication Engineering, vol. 9, no.6, p. 188-194, 2020. [12]. S. Hisham, M. Makhtar & A. A. Aziz. A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network. International Journal of Advanced Technology and Engineering Exploration, vol. 9, no. 95, p. 1366, 2022. [13]. O.E. Taylor, P.S. Ezekiel & D.J.S. Sako. A Deep Learning Based Approach for Malware Detection and Classification. iJournals: International Journal of Software & Hardware Research in Engineering (IJSHRE), vol. 9, no. 4, p. 32-40, 2021. [14]. B. Li, C. Chenli, X. Xu, Y. Shi & T. Jung. Dlbc: A deep learning-based consensus in blockchains for deep learning services. 2019. arXiv preprint arXiv:1904.07349. [15]. O.E. Taylor & P.S. Ezekiel. A robust system for detecting and preventing payloads attacks on web-applications using recurrent neural network (RNN). European Journal of Computer Science and Information Technology, vol. 10, no. 4, p. 1-13. 2022. [16]. B.S. Musa, U.M. Javed, A. Almogren, N. Javaid & A.M. Jamil. blockchain and stacked machine learning approach for malicious nodes’ detection in internet of things. Peer- to-Peer Networking and Applications, vol. 16, no. 6, p. 2811-2832, 2023. [17]. O.E. Taylor & P.S. Ezekiel. A smart system for detecting behavioural botnet attacks using random forest classifier with principal component analysis. European Journal of Artificial Intelligence and Machine Learning, vol. 1, no. 2, p. 11-16, 2022. [18]. S. Ismail, M. Nouman, W.D. Dawoud, & H. Reza. Towards a lightweight security framework using blockchain and machine learning. Blockchain: Research and Applications, 100174, 2023. [19]. F. O. Aghware, M. D. Okpor, W. Adigwe, C. C. Odiakaose, A. A. Ojugo, A. O. Eboka, P. O. Ejeh, O. E. Taylor, R. E. Ako & V. O. Geteloma. BloFoPASS: A blockchain food palliatives tracer support system for resolving welfare distribution crisis in Nigeria. Int. J. Informatics Commun. Technol, vol. 13, no. 2, p. 178-187, 2024.
