Abstract
Telecommunications services have become a constant in people’s lives. This has inspired fraudsters to carry out malicious activities causing economic losses to people and companies. Early detection of signs that suggest the possible occurrence of malicious activity would allow analysts to act in time and avoid unintended consequences. Modeling the behavior of users could identify when a significant change takes place. Following this idea, an algorithm for online behavior change detection in telecommunication services is proposed in this paper. The experimental results show that the new algorithm can identify behavioral changes related to unforeseen events.
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Notes
- 1.
Due to privacy and commercial policies of the telecommunication company, names, data, and other information that could lead to a personal or commercial information leakage are not offered. This was guaranteed by a Statement of Confidentiality signed between the research authors and TC.
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Acknowledgement
This research was supported by the Universidad Iberoamericana (Ibero) and the Institute of Applied Research and Technology (InIAT) by the project “Detection of phishing attacks in electronic messages using Artificial Intelligence techniques.”
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Herrera-Semenets, V., Hernández-León, R., Bustio-Martínez, L., van den Berg, J. (2022). Red Light/Green Light: A Lightweight Algorithm for, Possibly, Fraudulent Online Behavior Change Detection. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13613. Springer, Cham. https://doi.org/10.1007/978-3-031-19496-2_24
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