Abstract
Security is the main issue for real time systems, specially for financial and banking systems. Some of the customers who pay much attention to confidentiality and security on their network activities and transactions prefer to use the most secure channels, and for the others speed and the ease of services are more important. An optimized method should be a solution, but both strategies follow one common idea that any anomaly, abnormality, and intrusion should be handled in advance, as the reputation of each organization is based on trust. This paper proposes a new method with the aim of considering any anomaly in advance, in addition to partitioning strategy. The BFPM method makes use of the well-known Fuzzy C-Means clustering algorithm to evaluate whether packets or transactions are risky or not, and in what extent they will be risky in the near future. The proposed method aims to provide a flexible search space to cover prevention and prediction techniques at the same time.
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Yazdani, H., Choroś, K. (2019). Intrusion Detection and Risk Evaluation in Online Transactions Using Partitioning Methods. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_21
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DOI: https://doi.org/10.1007/978-3-319-98678-4_21
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