Cloud-Based Smart Contract Analysis in FinTech Using IoT-Integrated Federated Learning in Intrusion Detection

Venkatagurunatham Naidu Kollu, Vijayaraj Janarthanan, Muthulakshmi Karupusamy and Manikandan Ramachandran ()
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Venkatagurunatham Naidu Kollu: Department of Computer Science and Engineering, Dr.M.G.R. Educational and Research Institute, Chennai 600095, India
Vijayaraj Janarthanan: Department of Artificial Intelligence and Data Science, Easwari Engineering College, Ramapuram, Chennai 600089, India
Muthulakshmi Karupusamy: Department of Information Technology, PanimalarEngineering College, Poonamallee, Chennai 600123, India
Manikandan Ramachandran: School of Computing, SASTRA Deemed University, Thanjavur 613401, India

Data, 2023, vol. 8, issue 5, 1-21

Abstract: Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.

Keywords: intrusion detection system; FinTech; IoT federated learning architecture; smart contract analysis; cloud computing (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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