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Ann Appl Sport Sci 2023, 11 - Summer Supplementary: 0-0 |
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Arslan E, Güneş A. Fraud Detection in Enterprise Resource Planning Systems Using One-Class Support Vector Machine Combined with Convolutional Neural Network: The Case of Spor Istanbul. Ann Appl Sport Sci 2023; 11 (S1)
URL: http://aassjournal.com/article-1-1222-en.html
URL: http://aassjournal.com/article-1-1222-en.html
1- Department of Computer Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, Turkey , emraha@stu.aydin.edu.tr
2- Department of Computer Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, Turkey
2- Department of Computer Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, Turkey
Abstract: (1545 Views)
Background. Combining a One-Class Support Vector Machine (OCSVM) with a Convolutional Neural Network (CNN) is presented as a novel technique for detecting fraud in Enterprise Resource Planning (ERP) systems.
Objectives. The objective of this research is to develop a technique for detecting fraud in Enterprise Resource Planning (ERP) systems by combining a One-Class Support Vector Machine (OCSVM) with a Convolutional Neural Network (CNN), suitable for the Spor Istanbul ERP system.
Methods. This study examines the ERP system utilized by Spor Istanbul, the largest sports enterprise in Turkey, as a case study. The study utilizes a custom database of web-based program files to create a dataset of benign and malicious JavaScript applications. Firstly, the text and control flow graph of the program is analyzed. Secondly, the OCSVM method is applied as an outlier detection technique, and CNN is used as a classifier.
Results. The experimental results indicate that the proposed OCSVM-CNN approach achieves higher accuracy (96.78%) in detecting malicious scripts compared to using only CNN (94.8%).
Conclusion. The research contributes to the development of multi-layered ERP software architecture with AI decision support, improving fraud detection in ERP systems.
Objectives. The objective of this research is to develop a technique for detecting fraud in Enterprise Resource Planning (ERP) systems by combining a One-Class Support Vector Machine (OCSVM) with a Convolutional Neural Network (CNN), suitable for the Spor Istanbul ERP system.
Methods. This study examines the ERP system utilized by Spor Istanbul, the largest sports enterprise in Turkey, as a case study. The study utilizes a custom database of web-based program files to create a dataset of benign and malicious JavaScript applications. Firstly, the text and control flow graph of the program is analyzed. Secondly, the OCSVM method is applied as an outlier detection technique, and CNN is used as a classifier.
Results. The experimental results indicate that the proposed OCSVM-CNN approach achieves higher accuracy (96.78%) in detecting malicious scripts compared to using only CNN (94.8%).
Conclusion. The research contributes to the development of multi-layered ERP software architecture with AI decision support, improving fraud detection in ERP systems.
Keywords: Enterprise Resource Planning, Intelligent ERP, Forecasting, Demand Prediction, Decision Support System, Artificial Neural Networks, Cellular Neural Networks, Multi-Layered Software Architecture
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APPLICABLE REMARKS
APPLICABLE REMARKS
• This combined technique can effectively detect malicious scripts in Enterprise Resource Planning systems.
• The research proposes a new approach for feature extraction in ERP systems using machine learning algorithms.
• Future work could involve incorporating additional data sources, such as network traffic logs, to enhance the accuracy of anomaly detection in ERP systems.
• Real-time monitoring systems, integration with existing security systems, and the use of explainable AI techniques are potential directions to further improve the system's effectiveness and usability in detecting and responding to security threats and system errors.
• Overall, the research contributes to the development of multi-layered ERP software architecture with AI decision support, improving fraud detection and enhancing the success of ERP system implementations.
• The research proposes a new approach for feature extraction in ERP systems using machine learning algorithms.
• Future work could involve incorporating additional data sources, such as network traffic logs, to enhance the accuracy of anomaly detection in ERP systems.
• Real-time monitoring systems, integration with existing security systems, and the use of explainable AI techniques are potential directions to further improve the system's effectiveness and usability in detecting and responding to security threats and system errors.
• Overall, the research contributes to the development of multi-layered ERP software architecture with AI decision support, improving fraud detection and enhancing the success of ERP system implementations.
Type of Study: Original Article |
Subject:
Sport Management and its related branches
Received: 2023/05/1 | Accepted: 2023/06/24
Received: 2023/05/1 | Accepted: 2023/06/24
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