Preprint Article Version 1 This version is not peer-reviewed

Application of Data Mining on Web Usage Data for Security: WebSecuDMiner

Version 1 : Received: 2 September 2019 / Approved: 4 September 2019 / Online: 4 September 2019 (03:48:58 CEST)

How to cite: Khan, M.Z.A.; Park, J. Application of Data Mining on Web Usage Data for Security: WebSecuDMiner. Preprints 2019, 2019090040 (doi: 10.20944/preprints201909.0040.v1). Khan, M.Z.A.; Park, J. Application of Data Mining on Web Usage Data for Security: WebSecuDMiner. Preprints 2019, 2019090040 (doi: 10.20944/preprints201909.0040.v1).

Abstract

The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

Subject Areas

data mining; security; association rule; ECLAT

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