NDLI: A Methodology for Outlier Detection in Audit Logs for Financial Transactions

Content Provider	IEEE Xplore Digital Library
Author	Kanhere, P. Khanuja, H.K.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Eng., MMCOE, Pune, India (Kanhere, P.; Khanuja, H.K.)
Abstract	Database audit logs contain the information about database operations which are helpful to verify accuracy, lawfulness and to report risks. In financial systems, the audit logs should be monitored on continuous basis in order to detect and take action against any reasonably abnormal behavior. Outlier detection is a very important concept in the data mining which is useful in data analysis. Nowadays, a direct mapping can be found between the data outliers and the real world anomalies, and hence the outlier detection techniques can be applied to detect the abnormal activities in financial transactions. The purpose of the present study is to automate the process of abnormal activity detection and report generation. In the proposed framework, we will detect outliers based on rules and then we will apply Bayesian classification technique for abnormal activity risk classification. We will also generate user transaction profiles by obtaining user behavior according to historic transactions based on categorical or numerical attributes. Later we will monitor new transactions and will compare it against the corresponding user profile, in order to determine if the transaction is unusual. This framework will work on audit logs from a financial system, and thus will be useful for financial system auditors performing data analysis tasks.
Starting Page	837
Ending Page	840
File Size	149992
Page Count	4
File Format	PDF
ISBN	9781479968923
DOI	10.1109/ICCUBEA.2015.167
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-26
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Data analysis Databases Transaction Bayesian classification Banking Audit log Outlier detection Bayes methods Data mining Monitoring
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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