Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company

Volume 5, Issue 4, Page No 63-69, 2020

Author’s Name: Ma’shum Abdul Jabbar^1,a), Suharjito²

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¹Computer Science Department, Binus Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia
²Computer Science Department, Binus Online Learning, Bina Nusantara University, Jakarta 11480, Indonesia

^a)Author to whom correspondence should be addressed. E-mail: mashum.jabbar@binus.ac.id

Adv. Sci. Technol. Eng. Syst. J. 5(4), 63-69 (2020); DOI: 10.25046/aj050409

Keywords: Fraud, Call Detail Record, K-Means, DBSCAN

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Abstract

Fraud calls have a serious impact on telecommunications operator revenues. Fraud detection is very important because service providers can feel a significant loss of income. We conducted a fraud research case study on one of the operators that experienced fraud in 2009 and 2018. Call Detail Record (CDR) containing records of customer conversations such as source and destination number, call start time, duration of calls at the operator can be a source of information to use in fraud detection. The method used in this study uses machine learning with unsupervised learning techniques which are quite popular methods used in fraud detection. The purpose of this study is to propose an effective method that can be applied to detect fraud on the CDR. Variables used include caller number, number dialled, duration, fee and destination city of the dataset totalling 11,418 rows from record periods 01 to 31 May 2018. In analyzing our CDR using the K-Means and DBSCAN algorithms, we then evaluate the results to calculate accuracy by comparing to actual fraud data. Based on evaluations using confusion matrix on actual CDR fraud, we obtained the K-Means algorithm to show a better accuracy value to model fraud on telecommunications CDR compared to DBSCAN.

Received: 13 May 2020, Accepted: 19 June 2020, Published Online: 06 July 2020

References (31)

M. Arafat, A. Qusef and G. Sammour, “Detection of Wangiri Telecommunication fraud using ensemble learning” in 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019. https://doi.org/10.1109/JEEIT.2019.8717528
M. Liu, J. Liao, J. Wang and Q. Qi, “AGRM: Attention-based graph representation model for Telecom fraud detection” in ICC 2019-2019 IEEE International Conference on Communications (ICC), 2019. https://doi.org/10.1109/ICC.2019.8761665
S. Gee, Fraud and Fraud Detection: A Data Analytics Approach, Hoboken: John Wiley & Sons, Inc., 2015.
V. Jain, “Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining” International Journal of Information Technology, 9(3), 303-310, 2017. https://doi.org/10.1007/s41870-017-0036-5
T. Russell, Signaling system# 7 (Vol. 2), New York: McGraw-Hill, 2002.
C. S. Hilas, P. A. Mastorocostas and I. T. Rekanos, “Clustering of telecommunications user profiles for fraud detection and security enhancement in large corporate networks: a case study” Applied Mathematics & Information Sciences, 9(4), 1709, 2015. https://doi.org/10.12785/amis/090407
A. S. Yesuf, L. Wolos and K. Rannenberg, “Fraud risk modelling: requirements elicitation in the case of telecom services” in International Conference on Exploring Services Science, 2017. https://doi.org/10.1007/978-3-319-56925-3_26
S. Subudhi and S. Panigrahi, “A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network” Vietnam Journal of Computer Science, 5(3-4), 205-217, 2018. https://doi.org/10.1007/s40595-018-0116-x
I. Ighneiwa and H. Mohamed, “Bypass fraud detection: Artificial intelligence approach,” arXiv preprint arXiv:1711.04627, 2017.
E. Eifrem, “Graph databases: the key to foolproof fraud detection” Computer Fraud & Security, 2016(3), 5-8, 2016. https://doi.org/10.1016/S1361-3723(16)30024-0
M. Kolhar, A. Alameen and M. Gulam, “Performance evaluation of framework of VoIP/SIP server under virtualization environment along with the most common security threats” Neural Computing and Applications, 30(9), 2873-2881, 2018. https://doi.org/10.1007/s00521-017-2886-y
Q. Zhao, K. Chen, T. Li, Y. Yang and X. Wang, “Detecting telecommunication fraud by understanding the contents of a call” Cybersecurity, 1(1), 8, 2018. http://doi.org/10.1186/s42400-018-0008-5
S. Zoldi, “Using anti-fraud technology to improve the customer experience” Computer Fraud & Security, 2015(7), 18-20, 2015. https://doi.org/10.1016/S1361-3723(15)30067-1
E. Abba, A. M. Aibinu and J. K. Alhassan, “Development of multiple mobile networks call detailed records and its forensic analysis” Digital Communications and Networks, 5(4), 256-265, 2019. https://doi.org/10.1016/j.dcan.2019.10.005
J. Liu, B. Rahbarinia, R. Perdisci, H. Du and L. Su, “Augmenting telephone spam blacklists by mining large CDR datasets” in Proceedings of the 2018 on Asia Conference on Computer and Communications Security, 2018. https://doi.org/10.1145/3196494.3196553
A. Minessale II and G. Maruzzelli, Mastering FreeSWITCH, Packt Publishing Ltd, 2016.
K. C. Mondal and H. B. Barua, “Fault analysis and trend prediction in telecommunication using pattern detection: Architecture, Case Study and Experimentation” in International Conference on Computational Intelligence, Communications, and Business Analytics, 2018. https://doi.org/10.1007/978-981-13-8578-0_24
C. Gunavathi, R. S. Priya and S. L. Aarthy, “Big data analysis for anomaly detection in telecommunication using clustering techniques” in Information Systems Design and Intelligent Applications, 2019. https://doi.org/10.1007/978-981-13-3329-3_11
N. R. Al-Molhem, Y. Rahal and M. Dakkak, “Social network analysis in Telecom data” Journal of Big Data, 6(1), 99, 2019. https://doi.org/10.1186/s40537-019-0264-6
Y. Yu, X. Wan, G. Liu, H. Li, P. Li and H. Lin, “A combinatorial clustering method for sequential fraud detection” in In 2017 International Conference on Service Systems and Service Management, 2017. https://doi.org/10.1109/ICSSSM.2017.7996302
R. Hong, W. Rao, D. Zhou, C. An, Z. Lu and J. Xia, “Commuting Pattern Recognition Using a Systematic Cluster Framework” Sustainability, 12(5), 1764, 2020. https://doi.org/10.3390/su12051764
K. Sultan, H. Ali and Z. Zhang, “Call detail records driven anomaly detection and traffic prediction in mobile cellular networks” IEEE Access, 6, 41728-41737, 2018. https://doi.org/10.1109/access.2018.2859756
X. Min and R. Lin, “K-means algorithm: Fraud detection based on signaling data” in In 2018 IEEE World Congress on Services (SERVICES), 2018. https://doi.org/10.1109/services.2018.00024
J. Suntoro, Data Mining: Algoritma dan Implementasi dengan Pemrograman PHP, Elex Media Komputindo, 2019.
S. Adinugroho and Y. A. Sari, Implementasi Data Mining Menggunakan Weka, Universitas Brawijaya Press, 2018.
C. Xiong, Z. Hua, K. Lv and X. Li, “An improved K-means text clustering algorithm by optimizing initial cluster centers” in In 2016 7th International Conference on Cloud Computing and Big Data (CCBD), 2016. https://doi.org/10.1109/CCBD.2016.059
G. C. Ngo and E. Q. B. Macabebe, “Image segmentation using K-means color quantization and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for Hotspot Detection in Photovoltaic modules” in 2016 IEEE region 10 conference (TENCON), 2016. https://doi.org/10.1109/tencon.2016.7848290
F. O. Ozkok and M. Celik, “A new approach to determine Eps parameter of DBSCAN algorithm” International Journal of Intelligent Systems and Applications in Engineering, 5(4), 247-251, 2017. https://doi.org/10.18201/ijisae.2017533899
I. D. Id and E. Mahdiyah, “Modifikasi DBSCAN (Density-Based Spatial Clustering With Noise) pada Objek 3 Dimensi,” Jurnal Komputer Terapan, 3(1), 41-52, 2017. https://doi.org/10.13140/RG.2.2.22346.67529
M. Lenning, J. Fortunato, T. Le, I. Clark, A. Sherpa, S. Yi, P. Hofsteen, G. Thamilarasu, J. Yang, X. Xu, T. K. Hsiai, H. Cao and H. D. Han, “Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection” Sensors, 18(1), 61, 2018. https://doi.org/10.3390/s18010061
E. Alpaydin, Introduction to machine learning, MIT press, 2020.

Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company