@inproceedings{74847e73facc47f9b12b2c256da9a4b6,
title = "Scalable attack analysis of business process based on decision mining classification",
abstract = "Banking crime is one of the widespread phenomena in 2016 are closely associated with the used of computer-based technology and internet networks that constantly evolving. One of them is the burglary of customer accounts through the internet banking facility. To overcome this, we need a method of how to detect a conspiracy of bank burglary case of customer accounts. The way to scalable is by get a mining decision to get a decision tree and from the decision tree to get a decision attribute value to determine the level of anomalies. Then of all the attributes decision point is calculated rate of fraud. The rate of fraud is classified through level of security of attack by the attacker then entropy gain is used to calculate the relative effort between the level of attacks in the decision tree. The results show that the method could classify three levels of attacks and the corresponding entropy gains. The paper uses decision trees algorithm, alpha++ and dotted chart analysis to analyze an attack that can be scalable. The results of the analysis show that the accuracy achieved by 0.87%.",
keywords = "Bank, Business process, Dotted chart analysis, Event logs, Fraud, Process mining, Scalable, Security",
author = "Dewi Rahmawati and Riyanarto Sarno",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017 ; Conference date: 19-09-2017 Through 21-09-2017",
year = "2017",
month = dec,
day = "22",
doi = "10.1109/EECSI.2017.8239135",
language = "English",
series = "International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)",
publisher = "Institute of Advanced Engineering and Science",
editor = "Hatib Rahmawan and Mochammad Facta and Riyadi, {Munawar A.} and Deris Stiawan",
booktitle = "Proceedings - 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2017",
}