ارائه روشی برای شناسایی و کشف تقلب در کارت های اعتباری با استفاده از الگوریتم ترکیبی شبکه عصبی و رقابت استعماری

محورهای موضوعی : سامانه های پردازشی و ارتباطی چند رسانه ای هوشمند

جواد بالا کودهی ¹ (دانشگاه آزاد زنجان- دانشکده برق و کامپیوتر- زنجان- ایران)
محمد تحقیقی شربیان ² (استادیار، گروه کامپیوتر، دانشکده برق و کامپیوتر، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.)

تاریخ دریافت : 1400/05/18 تاریخ پذیرش : 1400/07/01 تاریخ انتشار : 1401/04/01

کلید واژه: کشف تقلب, ترکیبی شبکه عصبی, الگوریتم, کارت اعتباری, رقابت استعماری,

چکیده مقاله :

مقاله حاضر بـا مدل سـازی ریاضـی، فرآینـد اجتمـاعی-سیاسـی الگوریتم رقابت استعماری را در جهت ارائه یک الگوریتم قوی و کارا در حوزه بهینـه سـازی تشخیص به کارگرفته است. دراین الگو از الگوریتم بهینه سازی برای یـادگیری یک ساختار شبکه عصبی استفاده شده است. شبکه عصبی مورد استفاده در حل مسئله طبقه بندی دیتاهای بانکی به کار رفته است و اعمـال الگوریتم رقابت استعماری به مسئله یادگیری شـبکه عصـبی طبقـه بندی کننده نیز روش مطرح شده در کار است. ساختار ترکیبی و پلکانی مدل- مبتنی بر الگوی هوشمند سازی – بر اساس ساختار ارتقا ی سیستم پیشنهادی عمل می کند . در این پژوهش تشخیص تقلب در کارت‌های اعتباری با هدف شناسایی نرخ تقلب، بالا بردن دقت و اعمال کمترین نرخ خطای سیستم با استفاده شبکه‌های عصبی و ترکیب آن با الگوریتم رقابت استعماری همراه بوده است؛ همچنین استخراج ویژگی‌های مؤثر در ارزیابی تشخیص تقلب از دیگر اهداف این تحقیق می‌باشد.

چکیده انگلیسی:

Introduction: The imperialistic competition algorithm is a method in the field of evolutionary computing that deals with finding the optimal answer to various optimization problems. This algorithm provides an algorithm for solving mathematical optimization problems by mathematical modeling the socio-political evolution process. The imperialistic competition algorithm forms an initial set of possible answers. These initial answers are known as chromosomes in the genetic algorithm, particles in the particle swarm algorithm, and countries in the imperialistic competition algorithm. The imperialistic competition algorithm gradually improves these initial solutions (countries) with a special process that follows and finally provides the appropriate solution to the optimization problem. By imitating the process of the social, economic, and political evolution of countries and by mathematically modeling parts of this process, this algorithm presents operators in a regular form as an algorithm that can help solve complex optimization problems. In fact, this algorithm looks at the solutions of the optimization problem in the form of countries and tries to gradually improve these solutions during an iterative process and finally reach the optimal solution of the problem.Method: The proposed algorithm of this article (combined algorithm of neural network and colonial competition) has used the social-political process of the imperialistic competition algorithm with mathematical modeling in order to provide a strong and efficient algorithm in the field of diagnosis optimization.Findings: Our experiments proved that neural data classification using the transaction rejection option can lead us to a very low error rate, while we are looking for a very high detection rate. In this study, we reached an accuracy rate of 98.54, which is a higher accuracy rate compared to previous methods.Discussion: In this research, credit card fraud detection has been done with the aim of identifying the fraud rate, increasing the accuracy, and applying the lowest system error rate using neural networks and combining it with the colonial competition algorithm. Also, effective features were extracted in the evaluation of fraud detection. It can be concluded that the proposed classification system can have a very high detection performance in credit card financial transactions.

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