Culminating Project Title
Date of Award
Culminating Project Type
Information Assurance: M.S.
Information Assurance and Information Systems
Herberger School of Business
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Keywords and Subject Headings
Data mining, simbox fraud, Telecomunication, machine learning, international calls bypass fraud
Fraud detection in telecommunication industry has been a major challenge. Various fraud management systems are being used in the industry to detect and prevent increasingly sophisticated fraud activities. However, such systems are rule-based and require a continuous monitoring by subject matter experts. Once a fraudster changes its fraudulent behavior, a modification to the rules is required. Sometimes, the modification involves building a whole new set of rules from scratch, which is a toilsome task that may by repeated many times.
In recent years, datamining techniques have gained popularity in fraud detection in telecommunication industry. Unlike rule based Simbox detection, data mining algorithms are able to detect fraud cases when there is no exact match with a predefined fraud pattern, this comes from the fuzziness and the statistical nature that is built into the data mining algorithms. To better understand the performance of data mining algorithms in fraud detection, this paper conducts comparisons among four major algorithms: Boosted Trees Classifier, Support Vector Machines, Logistic Classifier, and Neural Networks.
Results of the work show that Boosted Trees and Logistic Classifiers performed the best among the four algorithms with a false-positive ratio less than 1%. Support Vector Machines performed almost like Boosted Trees and Logistic Classifier, but with a higher false-positive ratio of 8%. Neural Networks had an accuracy rate of 60% with a false positive ratio of 40%. The conclusion is that Boosted Trees and Support Vector Machines classifiers are among the better algorithms to be used in the Simbox fraud detections because of their high accuracy and low false-positive ratios.
AlBougha, Mhd Redwan, "Comparing Data Mining Classification Algorithms in Detection of Simbox Fraud" (2016). Culminating Projects in Information Assurance. 17.