Intelligence and Security Informatics: First European by Ronald R. Yager (auth.), Daniel Ortiz-Arroyo, Henrik Legind

By Ronald R. Yager (auth.), Daniel Ortiz-Arroyo, Henrik Legind Larsen, Daniel Dajun Zeng, David Hicks, Gerhard Wagner (eds.)

This publication constitutes the refereed complaints of the 1st ecu convention on Intelligence and defense Informatics, EuroISI 2008, held in Esbjerg, Denmark, in December 2008.

The 23 revised complete papers and a couple of revised poster papers provided have been conscientiously reviewed and chosen from forty eight submissions. The papers are prepared in topical sections on legal and social community research, intelligence research and information discovery, Web-based intelligence tracking and research, privateness security, entry keep an eye on, and electronic rights administration, malware and intrusion detection, in addition to surveillance and trouble management.

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Additional info for Intelligence and Security Informatics: First European Conference, EuroISI 2008, Esbjerg, Denmark, December 3-5, 2008. Proceedings

Example text

Application of existing tools on these complex socio-technical networks/systems is very demanding to winkle out the required information. , when the information is inclusive about the interaction among the nodes. However, the difficulty is that covert and terrorist networks are typically distributed across many boundaries for example from cities or countries and data about them is never complete-correct at a certain instant of time. Normally, a sampled snapshot data is available some of the links may be intentionally hidden.

46 U. Gl¨ asser and M. Vajihollahi Fig. 3. Geographic Environment Let Λv and Λe denote two additional sets of labels for vertices and edges respectively. The urban environment—integrating both the objective environment and the subjective environment for each of the agents—is defined as an attributed directed graph GEnv = (GGeoEnv , λ) where λ = (λv , λe ) abstractly represents the agent specific labeling of vertices and edges by means of two injective mappings as follows: – λv : AGENT×V → 2Λv , for each agent in AGEN T and each vertex in V , yields a non-empty set of vertex attributes, and – λe : AGENT×E → 2Λe , for each agent in AGEN T and each edge in E, yields a non-empty set of edge attributes.

Following paragraph provides the strength and limitations of SNA. – Strengths The most fundamental strength of SNA is that it provides a visual representation of the network structure. It allows the analysts to compare and identify previously unknown links. The knowledge gained through this process even can be used to forecast not only the individual activities of the actors but also of network/organization. – Limitations SNA is data dependent like most analytical software, therefore, correct and up to date data is essential for true analysis of a network/organization, therefore, if the data is incomplete or incorrect final product will be inaccurate.

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