Intelligence and Security Informatics: Pacific Asia by Sajimon Abraham, P. Sojan Lal (auth.), Michael Chau, G. Alan

By Sajimon Abraham, P. Sojan Lal (auth.), Michael Chau, G. Alan Wang, Xiaolong Zheng, Hsinchun Chen, Daniel Zeng, Wenji Mao (eds.)

This booklet constitutes the refereed complaints of the Pacific Asia Workshop on Intelligence and defense Informatics, PAISI 2011, held in Beijing, China, in July 2011. The eight revised complete papers and the five brief papers provided have been rigorously reviewed and chosen from a variety of submissions. The papers are equipped in topical sections on terrorism informatics and crime research; intelligence research and data discovery; info entry and safety; and infectious illness informatics.

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Extra info for Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2011, Beijing, China, July 9, 2011. Proceedings

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These parameters are extracted from text data by hand, and each time step corresponds to a day in the real world event. After running the model step by step, we get the netizen group’s concern and opinions as the output of the simulation. To test the model’s performance, the simulation results are then compared with results mined from real-time social media data using our sentiment analysis tool [20]. 48 Z. Tan, X. Li, and W. 1 Synutra Event Synutra event is a food safety event happened in August 2010, in which Synutra baby milk powder was suspected to cause precocious puberty.

And is it possible to find a similarity metric that can measure similarity between terrorist groups which can also hierarchically cluster all terrorist networks in a database? The answer is affirmative. COSM similarity measure is performing better than well known cosine and Jaccard similarity metrics as well as based on domain knowledge of terrorist groups. COSM tried to find similar terrorist networks based on crime and modus operandi similarity. Based on attributes of social networks data, a similarity metric can be developed using attribute similarity, like COSM does for terrorist networks.

Figure 5 was the example of blog dataset’s social network. 6 Conclusions In this paper, social network analysis method for cybercrime investigation was described. The from field and to field of e-mail’s header information were analyzed to obtain the participants’ communication pattern. Blog’s comment and reprinting were extracted to analyze blog user’s social relationship. E-mail’s header information or blog’s registration information were always ignored or forged. Therefore authorship identification methods to judge authorship’s authenticity were introduced.

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