Notice of Violation of IEEE Publication Principles
"Detecting Terror-Related Activities on the Web with Using Data Mining Techniques" by Mohammad Javad Hosseinpour and Mohammad Nabi Omidvar in the 2009 Second International Conference on Computer and Electrical Engineering (ICCEE 2009), 2009, pp. 152-157
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Using Data Mining Techniques for Detecting Terror-Related Activities on the Web" by Y. Elovici, A. Kandel, M. Last, B. Shapira, O. Zaafrany in the Journal of Information Warfare, Vol. 3, Issue 1, 2004, pp. 17-29
An innovative knowledge-based methodology for terrorist detection by using Web traffic content as the audit information is presented. The proposed methodology learns the typical behavior (`profile') of terrorists by applying a data mining algorithm to the textual content of terror-related Web sites. The resulting profile is used by the system to perform real-time detection of users suspected of being engaged in terrorist activities. The receiver-operator characteristic (ROC) analysis shows that this methodology can outperform a command based intrusion detection system.