Notice of Violation of IEEE Publication Principles: Practical approaches for analysis, visualization and destabilizing terrorist networks

Notice of Violation of IEEE Publication Principles

"Practical Approaches for Analysis, Visualization and Destabilizing Terrorist Networks"
by Nasrullah Memon and Henrik Legind Larsen
in Proceedings of the First International Conference on Availability, Reliability and Security (ARES), April 2006

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 has copied portions of text from the sources cited below. The lead author, Nasrullah Memon, was found to be solely responsible for the violation. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

"Untangling Criminal Networks: A Case Study"
by Jennifer Xu, Hsinchun Chen
Proceedings of the First NSF/NIJ Symposium Intelligence and Security Informatics, ISI, June 2003

"The Exploratory Construction of Database Views"
by M.N. Smith, P.J.H. King
Research Report BBKCS, School of Computer Science and Information Systems, Birbeck College, University of London, 2002


Traditionally most of the literature in social network analysis (SNA) has focused on networks of individuals. Although SNA is not conventionally considered as a data mining technique, it is especially suitable for mining a large volume of association data to discover hidden structural patterns in terrorist networks. After September 11 attacks, SNA has increasingly been used to study terrorist networks. As these covert networks share some features with conventional networks, they are harder to identify because they mask their transactions. The most complicating factor is that terrorist networks are often embedded in a much larger population (i.e., adversaries have links with both covert and innocent individuals). Hence, it is desirable to have tools to correctly classify individuals in covert networks so that the resources for isolating them will be used more efficiently. This paper uses centrality measures from complex networks to discuss how to destabilize adversary networks. We propose newly introduced algorithms for constructing hierarchy of the covert networks, so that investigators can view the structure of the ad hoc networks/atypical organizations, in order to destabilize the adversaries. The algorithms are also demonstrated by using publicly available dataset. Moreover we also demonstrate techniques for filtering graphs (networks)/detecting particular cells in adversary networks using a fictitious dataset.