DETECTING CREDIT CARD FRAUD WITH ADVANCED MACHINE LEARNING AND DEEP LEARNING METHODS
-д хадгалсан:
| -д хэвлэсэн: | International Journal of Communication Networks and Information Security vol. 17, no. 3 (2025), p. 192-198 |
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| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , |
| Хэвлэсэн: |
Kohat University of Science and Technology (KUST)
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| 003 | UK-CbPIL | ||
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Babu, P Bujji |u Department of CSE - AIML, Chalapathi Institute of Engineering and Technology, LAM, Guntur, Andhra Pradesh, India | |
| 245 | 1 | |a DETECTING CREDIT CARD FRAUD WITH ADVANCED MACHINE LEARNING AND DEEP LEARNING METHODS | |
| 260 | |b Kohat University of Science and Technology (KUST) |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns. Where cardholders are clustered into different groups based on their transaction amount. Then using sliding window strategy to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers are trained over the groups separately. And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift. In this paper, we worked with European credit card fraud dataset. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Credit card fraud | ||
| 653 | |a Neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Classification | ||
| 653 | |a Natural language processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Banking | ||
| 700 | 1 | |a Kumar, Yaganti Venkata Ajay |u Department of CSE - AIML, Chalapathi Institute of Engineering and Technology, LAM, Guntur, Andhra Pradesh, India | |
| 700 | 1 | |a Sumanths, Khagga |u Department of CSE - AIML, Chalapathi Institute of Engineering and Technology, LAM, Guntur, Andhra Pradesh, India | |
| 700 | 1 | |a Karun, Kommireddy |u Department of CSE - AIML, Chalapathi Institute of Engineering and Technology, LAM, Guntur, Andhra Pradesh, India | |
| 700 | 1 | |a Siddhartha, Indlamuri |u Department of CSE - AIML, Chalapathi Institute of Engineering and Technology, LAM, Guntur, Andhra Pradesh, India | |
| 773 | 0 | |t International Journal of Communication Networks and Information Security |g vol. 17, no. 3 (2025), p. 192-198 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3232790734/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3232790734/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3232790734/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |