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Intelligence and Security Informatics: Pacific Asia by Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan

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By Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan (auth.), Hsinchun Chen, Christopher C. Yang, Michael Chau, Shu-Hsing Li (eds.)

This publication constitutes the refereed court cases of the Pacific Asia Workshop on Intelligence and safeguard Informatics, PAISI 2009, held in Bangkok, Thailand, in April 2009.

The 10 revised complete papers, 7 revised brief papers including 1 keynote lecture have been conscientiously reviewed and chosen from a number of submissions. The papers are geared up in topical sections on terrorism informatics and crime research firm probability administration emergency reaction and surveillance info entry and safety, in addition to information and textual content mining.

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Extra resources for Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings

Example text

1 Introduction Terrorist attacks regularly occur in some cities; some of them are solved and identified for which terrorist organizations planned and realized them, whereas some of the terrorist attacks remain unsolved. Current terrorism informatics, which aims to help security officials using data mining techniques, is mainly focused on using social network analysis (SNA) for structural and positional analysis of terrorist networks [2] where required information is provided from non-crime data.

Authorship analysis in cybercrime investigation. In: Proceedings of the first international symposium on intelligence and security informatics, Tucson AZ USA, pp. tr Abstract. Detection of terrorist groups using crime data has few examples currently; this is because of lack of detailed crime data which contain terrorist groups’ attacks and activities. In this study, a novel prediction model; CPM is applied to a crime dataset which includes solved and unsolved terrorist events in Istanbul, Turkey between 2003 and 2005, aiming to predict perpetuators of terrorist events which are still remained unsolved.

When we represent those similarity scores then total simias , , could be measured by Euclidian distance of these scores; larity (2) Getting Euclidian distance for provides full matching of clusters, and within these clusters putting similarly behaving solved and unsolved crimes in more accurate clusters. 5 should be decided upon the size of clusters containing unsolved crimes to be predicted. If threshold value is decided lower, all predicted crimes will have similar counterparts with scores but it will also have false positives.

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