A COST-SENSITIVE ANOMALY DETECTION FRAMEWORK FOR ECONOMIC INFORMATION SECURITY MANAGEMENT
Abstract
The rapid digitalization of business environments has made information security a critical component of enterprise risk management. This paper develops a formal framework for anomaly detection as an instrument of economic information security management. The business information space is formalized as a tri-domain environment integrating transactional, behavioral, and system-level data. A hybrid detection architecture combines an unsupervised ensemble of Isolation Forest, Autoencoder, and One-Class SVM with a supervised classifier, enabling detection of both novel and known fraud patterns. A cost-sensitive loss function with analytical threshold optimization aligns detection decisions with expected financial loss minimization. A four-tier risk decision matrix translates detection outputs into economically grounded management responses.
References
IBM Security. (2025). Cost of a data breach report 2025. IBM Corporation. https://www.ibm.com/reports/data-breach (accessed April 2, 2026).
Cybersource. (2024). Global ecommerce payments & fraud report 2024. https://www.cybersource.com/content/dam/documents/campaign/fraud-report/global-fraud-report-2024.pdf (accessed April 2, 2026).
Gordon, L. A., & Loeb, M. P. (2002). The economics of information security investment. ACM Transactions on Information and System Security, 5(4), 438-457. https://doi.org/10.1145/581271.581274
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58. https://doi.org/10.1145/1541880.1541882
Aggarwal, C. C. (2017). Outlier analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-47578-3.
Goldstein, M., & Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLOS ONE, 11(4), Article e0152173. https://doi.org/10.1371/journal.pone.0152173.
Kumari, N., & Sami, A. (2024). A comprehensive investigation of anomaly detection methods in deep learning and machine learning: 2019-2023. IET Information Security, 2024, Article 8821891. https://doi.org/10.1049/2024/8821891.
Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud analytics using descriptive, predictive, and social network techniques. Wiley. https://doi.org/10.1002/9781119146841.
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems with Applications, 193, Article 116429. https://doi.org/10.1016/j.eswa.2021.116429
Rodrigues, V. F., Becker, L. B., Bizotto, B. L., Canedo, E. D., Cardoso-Pereira, I., & de Mendonça, F. L. L. (2022). Fraud detection and prevention in e-commerce: A systematic literature review. Electronic Commerce Research and Applications, 56, Article 101207. https://doi.org/10.1016/j.elerap.2022.101207.
Mutemi, A., & Bacao, F. (2024). E-commerce fraud detection based on machine learning techniques: Systematic literature review. Big Data Mining and Analytics, 7(2), 419-444. https://doi.org/10.26599/BDMA.2023.9020023.
Byrapu Reddy, S., Jayaraman, R., Rao, B. D., & Prashanthi, J. (2024). Effective fraud detection in e-commerce: Leveraging machine learning and big data analytics. Measurement: Sensors, 33, Article 101138. https://doi.org/10.1016/j.measen.2024.101138.
Zeng, Q., Lin, L., Jiang, R., Huang, W., & Lin, D. (2025). NNEnsLeG: A novel approach for e-commerce payment fraud detection using ensemble learning and neural networks. Information Processing & Management, 62(1), Article 103916. https://doi.org/10.1016/j.ipm.2024.103916.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. https://doi.org/10.1109/COMST.2015.2494502.
Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31. https://doi.org/10.1016/j.jnca.2015.11.016.
Maci, F., Coscia, P., Nicolardi, V., Ranieri, A., Rota, P., Sona, D., & Farinelli, A. (2024). A comprehensive investigation of clustering algorithms for UEBA. Frontiers in Big Data, 7, Article 1375818. https://doi.org/10.3389/fdata.2024.1375818.
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. Proceedings of the 8th IEEE International Conference on Data Mining, 413-422. https://doi.org/10.1109/ICDM.2008.17.
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443-1471. https://doi.org/10.1162/089976601750264965.
Höppner, S., Stripling, E., Baesens, B., vanden Broucke, S., & Verdonck, T. (2022). Instance-dependent cost-sensitive learning for detecting transfer fraud. European Journal of Operational Research, 297(1), 291-300. https://doi.org/10.1016/j.ejor.2021.05.028.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org (accessed April 8, 2026).
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