SpyNetMiner: An Outlier Analysis to Tag Elites in Clandestine Social Networks

S. Karthika, S. Bose and A. Kannan
Additional contact information
S. Karthika: Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai, Tamil Nadu, India
S. Bose: Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai, Tamil Nadu, India
A. Kannan: Department of Information Science and Technology, College of Engineering, Anna University, Chennai, Tamil Nadu, India

International Journal of Data Warehousing and Mining (IJDWM), 2014, vol. 10, issue 1, 32-54

Abstract: The homeland security has become a very significant consideration for all the Governments throughout the world. To improve the OPerational SECurity (OPSEC), multi relational graphs were introduced for Covert Network Analysis (CNA). In this paper, proposed SpyNetMiner system identifies the key players who maximally influence the covert network. Abnormality of the nodes is analyzed based on the profile generated using enhanced selection strategies. It further justifies the findings by presenting layman understandable explanation through feature extraction and semantic rule convertors. An event that brought a worldwide attention towards terrorism is the unforgettable 9/11 disaster. The covert network involved in this attack is used as dataset for SpyNetMiner. The performance of SpyNetMiner is compared to a similar system called as UNICORN and other conventional algorithms. The results evidently show that SpyNetMiner outperforms all existing methodologies in covert network analysis.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijdwm.2014010103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:10:y:2014:i:1:p:32-54

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().