Presenting An Approach For Recognizing The Detecting Hijacked Journals by Using Decision Tree Algorithms

This study was conducted for the analysis of hijacked journal and presenting new features to apply them and reduce internet frauds.
To reach this goal, First according to studies done on the websites the of authentic and hijacked journals, the features of this kind of attacks are extracted and then a training dataset is created. The number of collected data records is 104 were collected and analyzed using WEKA data mining tools. The results showed that the applied method has an error rate of 1 percent.
Findings: The collected data of this research was analyzed by Weka data mining software. The algorithms used in this survey were developed by the Decision Tree Algorithm. An algorithm with a lower error rate is selected. The results showed that the applied method has an error rate of 9 percent.
The previous studies showed an increase in the number of fake publishers and hijacked journals. This article is dealt with finding a way to identify these type of journals. The results obtained from the data collected using a decision tree and the analysis thereof was shown.