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Intelligence and Security Informatics: Pacific Asia by Aaron Hunter (auth.), G. Alan Wang, Xiaolong Zheng, Michael

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By Aaron Hunter (auth.), G. Alan Wang, Xiaolong Zheng, Michael Chau, Hsinchun Chen (eds.)

This ebook constitutes the refereed court cases of the Pacific Asia Workshop on Intelligence and defense Informatics, PAISI 2013, held in Beijing, China, in August 2013 along with the foreign Joint convention on synthetic Intelligence (IJCAI 2013). The four revised complete papers offered including 7 brief papers have been rigorously reviewed and chosen from quite a few submissions. The papers are prepared in topical sections on info sharing and data/text mining; terrorism informatics; network-based info analytics; and data entry and security.

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Additional resources for Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2013, Beijing, China, August 3, 2013. Proceedings

Example text

Since the research is conducted in a Chinese context, the system is established to primarily process Chinese forum threads. Chinese sentences at the pre-process stage are broken into Chinese words or phrases using ICTCLAS [26-27]. After that, the system processes posts and their comments in parallel to generate separated classification decision. Forum threads are therefore analyzed by two models, namely affect analysis [5] and collective intelligence. The affect analysis classifies a thread using its main post only, whereas collective intelligence approach examines comments to generate the mainstream opinion from reply authors [13].

Based on a set of random variables, decision variables, utility functions and their dependencies, it allows computing the optimal order of decisions, observations, and the expected utility of the problem. Creating the link between the optimal attacks in attack graphs and solving UIDs can be beneficial for research in both the AI and security domains. For security research, this link allows using the existing UID solvers for fast prototyping of attack graph analysis tools and the theory and algorithms developed for UIDs can help in designing more efficient specific algorithms for attack graphs.

We were adding 1 to 6 workstations with one remote client vulnerability on each. The Chain network is the other network in Figure 3 with one vulnerability per machine, with 0 to 5 copies of the middle segment and nodes reachable only along direct lines. 5 and 1, and their costs were random integers between 0 and 10. The results are summarized in Figure 4. The table presents the number of AND and OR nodes in the generated attack graphs for individual problem sizes and the graph Internet Internet Fig.

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