ATM Transaction Status Analysis and Anomaly Detection
This article mainly studies ATM transaction feature analysis and anomaly detection. Select the trading success rate, transaction response time and other characteristic parameters, analyze the relationship between transaction response time and transaction success rate. After comparing the fuzzy C-means clustering algorithm and the K-means clustering algorithm, K-means clustering algorithm was used to classify the transaction response time. Then design trading anomaly detection program. The use of naive Bayesian classifier for data classification can determine the alarm level. And using Gaussian distribution and Laplace smoothing calibration to increase model accuracy to reduce false alarm. The use of MATLAB programming to get the following result: When the transaction response time is between 0 ~ 85.57, the system predicts to be successful. When the transaction response time between 85.57 ~ 212.31, the system predicts a warning. When the transaction response time between 212.31 ~ 1007.8, the system predicts that the alarm.
Liu Jiafeng, Zhao Wei & Zhu Hailong. (2014). Pattern recognition[M]. Harbin: Harbin Institute of Technology Press.
Zhao Yuan, Wang Jie, & Xiong Yanjiao, et al. (2016). A gaussian mixture model of multivariables in power system reliability assessment. Automation of Electric Power Systems, 40(1), 66-71,80.
Saunders C S．(2014). Point estimate method addressing correlated wind power for probabilistic optimal power flow. IEEE Transactions on Power Systems, 29(3), 1045-1054.
Ren Zhouyang, Li Wenyuan & Billinton R,et al．(2015). Probabilistic power flow analysis based on the stochastic response surface method. IEEE Transactions on Power Systems, 31(3), 2307-2315.
Copyright (c) 2018 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.