A forensics method of web browsing behavior based on association rule mining
Yiyun Zhang, Guolong Chen · 2014
With the development of network, web forensics is becoming more and more important due to the rampant cybercrime. In this paper, a forensics method of web browsing behavior based on association rule mining is presented. The method aims at providing the necessary data support to build the behavior pattern library for investigation. The records of the user's browsing history are collected to be analyzed. The obtained original data are pretreated to transactional data which are suitable for association rule mining. Frequent browsing time and frequent web browsing sequences are obtained from the transactional data by Apriori algorithm. The mining results are helpful for identification and recognition of anonymous or suspicious web browsing behavior patterns.