DocumentCode
3090929
Title
Notice of Violation of IEEE Publication Principles
Detecting Terror-Related Activities on the Web with Using Data Mining Techniques
Author
Hosseinpour, M.J. ; Omidvar, M.N.
Author_Institution
Islamic Azad Univ. Estahban-Branch, Estahban, Iran
Volume
2
fYear
2009
fDate
28-30 Dec. 2009
Firstpage
152
Lastpage
157
Abstract
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.
"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.
Keywords
Internet; data mining; security of data; sensitivity analysis; terrorism; text analysis; Web traffic content; World Wide Web; data mining techniques; innovative knowledge-based methodology; intrusion detection system; receiver-operator characteristic analysis; terror-related Web sites; terror-related activity detection; terrorism; Computer networks; Data mining; Functional analysis; Information analysis; Internet; Intrusion detection; Java; Law enforcement; Real time systems; Terrorism; anomaly detection; data mining; terrorist trend detection; user modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Electrical Engineering, 2009. ICCEE '09. Second International Conference on
Conference_Location
Dubai
Print_ISBN
978-1-4244-5365-8
Type
conf
DOI
10.1109/ICCEE.2009.46
Filename
5380205
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