Abstract
Nowadays, security incidents of financial IT services and internet banking hacking against the financial companies have occurred continuously, resulting in a loss of the financial IT systems. Accordingly, this paper based on ‘framework standards of financial transaction detection and response’ was designed to propose of anomaly Electronic Financial Transaction (EFT) pattern recognition and response for the method to detect anomaly prior behaviors and transaction patterns of users. It was applied to moving average based on the statistical basis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Moving average is one of the methods to determine the trend value. For time series of \( X_{1} ,X_{2} , \ldots X_{t} \), and moving average \( \overline{{X_{m} }} \) in the period range of m at the time t is derived as follows. \( \overline{{X_{m} }} = \left( {X_{t} + X_{t + 1} + \ldots + X_{{t + \left( {m - 1} \right)}} } \right)/m \), \( \left( {t = 1,2, \ldots \left( {t - m} \right)} \right) \). When new series of \( \overline{{X_{m + 1} }} ,\overline{{X_{m + 2} }} \) are made in this way, the change in current time series represents an even trend.
References
Rueda LG, Oommen BJ (2002) On optimal pairwise linear classifiers for normal distributions: the two-dimensional case. IEEE Trans Pattern Anal Mach Intell 24(2):274–280
Youguo P (2007) The frame of cognitive pattern recognition. In: Proceedings of the 26th Chinese control conference, Zhangjiajie, Hunan, pp 694–696, 26–31 Jul 2007
Chan K, Fu W (1999) Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE international conference on data engineering, Sydney, pp 126–133
Zhang HQ, Cai QS (2003) Time series similar pattern matching based on wavelet transform. Chinese J Comput 26(3):373–377
Zhu T (2006) Suspicious financial transaction detection based on empirical mode decomposition method. In: Proceedings of the 2006 IEEE Asia-Pacific conference on services computing (APSCC’06), Guangzhou, Guangdong. IEEE Computer Society, pp 300–304
Telecommunications Technology Association (TTA) (2011) Fraud detection and response framework in electronic financial transaction system, TTAK.KO-12.0178
NIST/SEMATECH (2012) e-Handbook of Statistical Methods: what are moving or smoothing techniques? Average, Apr. 2012. http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc42.htm
Acknowledgments
This work is supported by the Korea Information Security Agency (H2101-12-1001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Kim, A.C., Park, W.H., Lee, D.H. (2013). A Framework for Anomaly Pattern Recognition in Electronic Financial Transaction Using Moving Average Method. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_12
Download citation
DOI: https://doi.org/10.1007/978-94-007-5860-5_12
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5859-9
Online ISBN: 978-94-007-5860-5
eBook Packages: EngineeringEngineering (R0)