Computer Networks and Distributed Systems
Touraj BaniRostam; Hamid BaniRostam; Mir Mohsen Pedram; Amir Masoud Rahamni
Volume 7, Issue 3 , August 2021, , Pages 157-166
Abstract
Abstract—Several studies have been presented to solve challenges of electronic card (e-card) fraud that the two main purposes of these studies are to identify types of e-card fraud and to investigate the methods used in bank fraud detection. To achieve this purpose, one of the most common methods ...
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Abstract—Several studies have been presented to solve challenges of electronic card (e-card) fraud that the two main purposes of these studies are to identify types of e-card fraud and to investigate the methods used in bank fraud detection. To achieve this purpose, one of the most common methods of detecting fraud is to investigate suspicious changes in user behavior. Supervised learning techniques help to find anomalies by analyzing user behavioral history based on past transaction patterns in fraud detection systems. One of challenging issues in detecting fraud is to consider the change of customer behavior and the ability of fraudsters to devise new patterns of fraud, which makes unsupervised learning techniques popular for detecting unknown and new frauds. In this paper, the concepts of fraud, types of banking fraud along with their challenges, different form of fraud and banks' data research tools for early identification have been examined, then a review of the researches on fraud detection will be conducted. This paper aims to introduce fraud detection techniques and methods that have provided appropriate results in the big data environment. Finally, the fraud detection algorithms and proposed methods of related works presented in this paper, will be fully compared on a common dataset in terms of parameters such as speed of fraud detection, accuracy, and cost (hardware and network resources). Ensemble Meta-Learning can be used alone to build a stronger classifier. These techniques have been relatively successful in detecting fraud and reducing costs.
Computer Networks and Distributed Systems
Elham shamsinejad; Mir Mohsen Pedram; Amir Masoud Rahamni; Touraj BaniRostam
Volume 7, Issue 3 , August 2021, , Pages 187-196
Abstract
By increasing access to high amounts of data through internet-based technologies such as social networks and mobile phones and electronic devices, many companies have considered the issues of accessing large, random and fast data along with maintaining data confidentiality. Therefore, confidentiality ...
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By increasing access to high amounts of data through internet-based technologies such as social networks and mobile phones and electronic devices, many companies have considered the issues of accessing large, random and fast data along with maintaining data confidentiality. Therefore, confidentiality concerns and protection of specific data disclosure are one of the most challenging topics. In this paper, a variety of data anonymity methods, anonymity operators, the attacks that can endanger data anonymity and lead to the disclosure of sensitive data in the big data have been investigated. Also, different aspects of big data such as data sources, content format, data preparation, data processing and common data repositories will be discussed. Privacy attacks and contrastive techniques like k anonymity, neighborhood t and L diversity have been investigated and two main challenges to use k anonymity on big data will be identified, as well. Two main challenges to use k anonymity on big data will be identified. The first challenge of confidential attributes can also be as pseudo-identifier attributes, which increases the number of pseudo-identifier elements, and it may lead to the loss of great information to achieve k anonymity. The second challenge in big data is the unlimited number of data controllers are likely to lead to the disclosure of sensitive data through the independent publication of k anonymity. Then different anonymity algorithms will be presented and finally, the different parameters of time order and the consumable space of big data anonymity algorithms will be compared.