Helpful and Efficient Framework for Classification and Analysis of various Fraud Detection Approaches from the perspective of Time and Features
سال انتشار: 1395
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 727
فایل این مقاله در 8 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
CEPS04_005
تاریخ نمایه سازی: 11 مرداد 1396
چکیده مقاله:
Fraud detection is one of the ways to cope with damages of fraudulent activities that due to the rapid development of Internet technology and the rapid rise of electronic business have increased. In this paper, first the concept of fraud and fraud detection position will be expressed and then a helpful and efficient framework for classification of various fraud detection approaches will be presented. Investigation of the proposed methods in this field indicates the existence of various approaches in them for fraud detection and perception of existing concepts. Lack of comprehensive and valuable classification in this regard and specified evaluation criteria in them have faced the researchers with a new challenge. Therefore, in this paper, in addition to the explanation and identification of fundamental approaches to detect fraud from the perspective of time and features, appropriate criteria for evaluating these methods were described. Finally, the proposed approaches were evaluated based on the presented criteria. Using the systematic and structured framework proposed in this paper can be beneficial in detecting more accurate fraud detection methods and applying them in a more principled way. Additionally, using the mentioned framework lays the appropriate base for a comparative study of the existing methods in this regard.
کلیدواژه ها:
نویسندگان
Zahra Karimizandian
Data Mining Lab, Department of Computer Engineering, Alzahra University, Tehran, Iran
Mohammadreza Keyvanpour
Data Mining Lab, Department of Computer Engineering, Alzahra University, Tehran, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :