بكارگيري الگوريتم هاي درخت تصميم گيري جهت كشف رفتارهاي مشكوك در بانكداري اينترنتي

Introducing a Model for Suspicious Behaviors Detection in Electronic Banking by Using Decision Tree Algorithms

دگرگوني جهان به واسطه گسترش فناوري اينترنت، رقابتي دانش محور را در عرصه تجارت الكترونيكي به وجود آورده است. به‌طوري كه با افزايش نرخ مبادلات تجاري، تضمين امنيت، منوط به تحقق نظام پوياي بانكداري الكترونيكي است. بانكداري اينترنتي به عنوان يك فرصت بالقوه و ركن اساسي، در فضاي سايبر با تهديدهاي گوناگوني مواجه است كه يكي از اين چالش ها، عدم قطعيت در تضمين امنيت تراكنش ها و وجود رفتارهاي غيرمتعارف از سوي شيادان الكترونيكي است. از آنجايي كه رفتار كاربران در سامانه اينترنتي با عدم قطعيت همراه بوده و سوابق تراكنش ها، تنها راه كنترل اين حركات است، دانش استنتاجي خبرگان و دسته بندي الگوي رفتاري كاربران توسط الگوريتم درخت تصميم ، جهت كشف تقلب و رفتارهاي مشكوك راهگشاست. در اين مقاله، ابتدا متغيرهاي موثر در توليد قوانين رفتاري تعيين شده است و در نهايت، روند چهار الگوريتم Chaid، ex_Chaid، C4.5 و C5.0 مورد مقايسه قرار گرفته است. نتايج پژوهش نشان مي دهد كه الگوريتم C5.0 با دقت 91 درصد مي تواند به عنوان روش ماشيني مطمين جهت كشف الگوهاي مشكوك موجود روي تراكنش هاي بانك محسوب شود.

Change the world through information technology and Internet development, has created competitive knowledge in the field of electronic commerce, lead to has been increased competitive potential among organizations. In this condition The increasing rate of commercial deals developing guaranteed with speed and light quality is due to provide dynamic system of electronic banking until by using modern technology to facilitate electronic business process. Internet banking is enumerate as a potential opportunity the fundamental pillars and determinates of e-banking that in cyber space has been faced with various obstacles and threats. One of this challenge is complete Uncertainty in security guarantee of financial transactions also exist of suspicious and unusual behavior with mail fraud for financial abuse. Now various systems because of intelligence mechanical methods and data mining technique has been designed for fraud detection in users’ behaviors and applied in various industrial such as insurance, medicine and banking. Main of article has been recognizing of unusual users behaviors in e-banking system. Therefore, detection behavior user and categories of emerged patterns to paper the conditions for predicting unauthorized penetration and detection of suspicious behavior. Since detection behavior user in internet system has been Uncertainty and records of transactions can be useful to understand these movement and Therefore among machine method, Decision tree technique is considered common tool for classification and prediction, therefore in this research at first has determinate banking effective variable and weight of everything in internet behaviors production and In continuation combining of various behaviors manner draw out such as the model of inductive rules to provide ability recognizing of different behaviors. at least trend of four algorithm Chaid, ex_Chaid, C4.5, C5.0 has compared and evaluated for classification and detection of exist models on the real transactions of private bank.