International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Cloud based Fraud Detection through Semantic Data Mining Rumaiya Naz1, Sakshi K2, Saniya Khanum3, Shivanagouda M Totada4 , Mr. Rajesh T H5 1Rumaiya Naz ,PES
Institute of Technology and Management, Sagar Road, Shimoga
2Sakshi K , PES Institute of Technology and Management, Sagar Road, Shimoga
3Saniya Khanum , PES
Institute of Technology and Management, Sagar Road, Shimoga Mr.Rajesh T H Dept. of CS Engineering, PES Institute of Technology and Management,Karnataka ---------------------------------------------------------------------***--------------------------------------------------------------------everyday items [3]. Monetary extortion is known as Abstract - This abstract outlines a project focused on
monetary maltreatment which is a major worry in financial society making colossal misfortunes the economy of states, associations, corporate areas or even people. It very well may be characterized as a demonstration of unfair or unlawful way of behaving, bringing about a useful increase to one or the other individual or association from dishonest and unlawful ways [4]. Extortion location methods were acquainted with distinguish strange exercises, that happened in past exchanges planning to find cases that fraudsters mean to disregard the qualities that the associations make in return for providing administrations. Different strategies have been proposed to distinguish extortion, yet these techniques are infeasible because of the steady advancement of new techniques created by fraudsters or in new advancements like digital money. As indicated by the way that any Web based business framework that includes online exchanges, for example, monetary administrations is helpless against be undermined by fraudsters [5]. Hence, hostile to extortion has turned into a subject of interest by numerous researchers to investigate the issues connected with this field. The significant issues of misrepresentation roused researchers to foster location techniques or even gauge extortion risk.
advancing fraud detection capabilities through the integration of cloud computing and semantic data mining. In response to the evolving landscape of financial security challenges, this initiative aims to harness the power of these technologies for more accurate and efficient fraud detection. The project builds on the premise that cloud-based solutions provide scalable and flexible infrastructure, enhancing the processing and analysis of large datasets crucial for fraud detection. Semantic data mining, with its ability to extract meaningful patterns, is poised to contribute significantly to the project's objectives. By leveraging the synergy between cloud computing and semantic data mining, the project seeks to develop an innovative framework capable of detecting and preventing fraudulent activities in real-time. This abstract introduces a forward-looking initiative that holds promise for addressing the dynamic nature of contemporary fraud threats in the realm of financial security Key Words: CLOUD COMPUTING, DATA MINING, FRAUD DETECTION, DATA MINING TECHNIQUE, FINANCIAL SECURITY
1.INTRODUCTION In the specific context of fraud detection, data mining emerges as a critical tool for unraveling intricate patterns and anomalies within vast datasets. As businesses and financial transactions become increasingly digitized, the need for effective fraud detection mechanisms has never been more pronounced. Data mining serves as a cornerstone in this domain, providing a sophisticated approach to identifying fraudulent activities that may elude traditional statistical methods.
Information mining is a methodology utilized in separating significant information from a given dataset utilizing at least one methodologies such as factual, AI, numerical or computerized reasoning methods. Among these methodologies, various types of procedures can be applied for monetary extortion like Innocent Bayes (NB), support vector machine (SVM), Calculated Relapse (LR), furthermore, a lot more [11]. By and large, information mining is generally used to find monetary fakes that can be arranged into six classifications like grouping, perception, exception discovery, bunching, relapse, and expectation [4].
Fraud detection through data mining extends its application to various sectors, with finance and e-commerce being primary beneficiaries. The approach involves leveraging algorithms such as clustering and decision trees to discern patterns indicative of potential fraud. This is particularly crucial in uncovering irregularities such as unauthorized transactions, identity theft, and deceptive financial activities.
1.1 Objectives The key objectives include: 1.
Monetary extortion is a fundamental issue that influences both the money area and daily existence and assumes a basic part in influencing trustworthy qualities and confidences in monetary areas as well as the people's cost for many
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Enhanced Detection Accuracy: Develop and implement advanced semantic data mining algorithms within a cloud infrastructure to improve the accuracy of fraud detection. This involves identifying intricate patterns and anomalies indicative of fraudulent behavior across diverse datasets.
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