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Anjana Verma, Nitya Khare
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- Assistant Professor, Assistant Professor, Department of Computer Science & Engineering, Sagar Institute of Research & Technology (SIRT), Bhopal, Department of Computer Science & Engineering, Sagar Institute of Research & Technology (SIRT), Bhopal, Madhya Pradesh, Madhya Pradesh, India, India
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Abstract
nWith the alarming rise in global financial fraud, necessitating substantial annual losses, modern techniques for fraud detection are continuously evolving across various business domains. Fraud detection involves constant monitoring of user activities to estimate, perceive, or prevent undesirable behaviour. Cloud Computing emerges as a promising solution, accelerating application deployment, fostering creativity and innovation, reducing costs, and enhancing overall business acumen. This paper introduces a cloud-driven approach to fraud detection, specifically tailored for credit card transactions. Leveraging machine learning applications deployed on Google Cloud Engine, the study employs classification techniques on a dataset comprising 284,807 credit card transactions. The chosen techniques adeptly process both numerical and categorical datasets. Decision tree and random forest classifiers are implemented for classification, and their performance is meticulously evaluated and compared through various metrics. The findings highlight the efficacy of the proposed cloud-driven strategy, shedding light on the comparative performance of decision tree and random forest classifiers in bolstering the security of credit card transactions. This research contributes valuable insights to the ongoing endeavours aimed at fortifying fraud detection mechanisms, providing a nuanced understanding for organizations grappling with the intricate challenges posed by fraudulent activities in the contemporary digital landscape.
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Keywords: Cloud computing, private cloud, machine learning application, security, SSH (Secure Shell)
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References
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | January 25, 2024 | |
| Accepted | February 1, 2024 | |
| Published | April 4, 2024 |
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