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Intelligence and Security Informatics, 2008. ISI 2008. IEEE International Conference on

Date 17-20 June 2008

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Displaying Results 1 - 25 of 90
  • ISI 2008 Preface Welcome Message from Conference Co-chairs

    Page(s): ix - x
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  • Message from the Program Chairs

    Page(s): xi
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  • ISI 2008 Conference Organizers

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  • ISI 2008 Program Committee

    Page(s): xv - xvii
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  • IEEE ISI 2008 Sponsors

    Page(s): xviii
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  • IEEE ISI 2008 Detailed Program

    Page(s): xix - xxiii
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  • Countering terrorism

    Page(s): xxiv
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  • Cyber crime and challenges for crime investigation in the information era

    Page(s): xxv - xxvi
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  • Homeland security data mining using social network analysis

    Page(s): xxvii
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  • Data mining for security applications: Mining concept-drifting data streams to detect peer to peer botnet traffic

    Page(s): xxix - xxx
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  • Probabilistic frameworks for privacy-aware data mining

    Page(s): xxxi - xxxii
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  • Data mining for social network analysis

    Page(s): xxxiii - xxxiv
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  • Social Computing: Fundamentals and applications

    Page(s): xxxv - xxxviii
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  • Intelligent pattern recognition and biometrics

    Page(s): xxxix - xl
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  • Real time intrusion prediction, detection and prevention programs

    Page(s): xli - xlii
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  • Table of contents

    Page(s): iii - viii
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  • Analysis of cyberactivism: A case study of online free Tibet activities

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (758 KB) |  | HTML iconHTML  

    Cyberactivism refers to the use of the Internet to advocate vigorous or intentional actions to bring about social or political change. Cyberactivism analysis aims to improve the understanding of cyber activists and their online communities. In this paper, we present a case study of online Free Tibet activities. For web site analysis, we use the inlink and outlink information of five selected seed URLs to construct the network of Free Tibet web sites. The network shows the close relationships between our five seed sites. Centrality measures reveal that tibet.org is probably an information hub site in the network. Further content analysis tells us that common hub site words are most popular in tibet.org whereas dalailama.com focuses mostly on religious words. For forum analysis, descriptive statistics such as the number of posts each month and the post distribution of forum users illustrate that the two large forums FreeTibetAndYou and RFAnews-Tibbs have experienced significant reduction in activities in recent years and that a small percentage of their users contribute the majority of posts. Important phrases of several long threads and active forum users are identified by using mutual information and TF-IDF scores. Such topical analyses help us understand the topics discussed in the forums and the ideas and interest of those forum users. Finally, social network analyses of the forum users are conducted to reflect their interactions and the social structure of their online communities. View full abstract»

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  • Developing ideological networks using social network analysis and writeprints: A case study of the international Falun Gong movement

    Page(s): 7 - 12
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    The convenience of the Internet has made it possible for activist groups to easily form alliances through their Websites to appeal to wider audience and increase their impact. In this study, we investigate the potential of using social network analysis (SNA) and Writeprints to discover the fusion of activitst ideas on the Internet, focusing on the Falun Gong movement. We find that network visualization is very useful to reveal how different types of Websites or ideas are associated and, in some cases, mixed together. Furthermore, the measures of centrality in SNA help to reveal which Websites most prominently link to other Websites. We find that Writeprints can be used to identify the ideas which an author gradually introduces and combines through a series of messages. View full abstract»

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  • Detecting deception in testimony

    Page(s): 13 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (208 KB) |  | HTML iconHTML  

    Several models for deception in text, based on changes in usage frequency of certain classes of words, have been proposed. These are empirically derived from settings in which individuals are asked to lie or be truthful in freeform text. We consider the problem of detecting deception in testimony, where the content generated must necessarily be responsive to questions, where there is the opportunity for immediate followup if the possibility of deception is detected by the questioner, and where those who have reasons to be deceptive have time and motivation to rehearse potential answers. Using the testimony to the Gomery Commission, a situation in which many witnesses had some motivation to be deceptive, we propose and validate a model for deception in testimony. We achieve substantial (80%) agreement with media estimates of who was motivated to testify in a deceptive way. View full abstract»

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  • Subverting prediction in adversarial settings

    Page(s): 19 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (283 KB) |  | HTML iconHTML  

    We show that two mainstream prediction techniques, support vector machines and decision trees, are easily subverted by inserting carefully-chosen training records. Furthermore, the relationship between the properties of the inserted record(s) and the regions for which the predictor will subsequently misclassify can be inferred, so desired misclassifications can be forced. In adversarial settings, it is plausible that manipulation of this kind will be attempted, so this has implications for the design of prediction systems and the use of off-the-shelf technology, especially as support vector machines are one of the most powerful prediction algorithms known. View full abstract»

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  • Analyzing content development and visualizing social interactions in Web forum

    Page(s): 25 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1596 KB) |  | HTML iconHTML  

    Web forums provide platforms for any Internet users around the world to communicate with each other and express their opinions. In many of the discussions in Web forums, it involves issues related to terrorism and crime. Some participants are even using the platform to propagandize their ideology or recruit members to commit crime. In this work, we propose a Web forum analysis system to analyze the content development and visualize the social interactions in Web forum. View full abstract»

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  • Link analysis based on webpage co-occurrence mining - a case study on a notorious gang leader in Taiwan

    Page(s): 31 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (673 KB) |  | HTML iconHTML  

    The rapid development and integration of computer and communication technology have made the Internet one of the major media for communication. Nowadays, the Internet and world-wide-web (WWW) are used in every facet of modern society. This research proposes to develop a link analysis method based on Web page co-occurrence mining. The Google Internet search engine is used to gather Web pages relevant to a certain search subject. The proper nouns on each relevant page are then extracted using the Chinese Word Segmentation System, developed by Academia Sinica, Taiwan. The co-occurrence data of proper nouns is then analyzed and used for constructing link charts for visualization and further analysis. View full abstract»

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  • A study on early decision making in objectionable web content classification

    Page(s): 35 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (387 KB) |  | HTML iconHTML  

    This study proposed early decision heuristics for objectionable content classification using an inverse chi-square classifier. The experimental results indicated that only examining the title plus 10% of a Web pagepsilas content can cost-effectively achieve an average precision of 93%. More importantly, the F1 measure achieved its best when the title plus 60% of the body was examined. The proposed early decision making heuristics can serve as the trade-off baseline for real-time online filtering. View full abstract»

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  • Information sharing and privacy protection of terrorist or criminal social networks

    Page(s): 40 - 45
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (418 KB) |  | HTML iconHTML  

    Terrorist or criminal social network analysis is helpful for intelligence and law enforcement force in investigation. However, individual agency usually has part of the complete terrorist or criminal social network and therefore some crucial knowledge is not able to be extracted. Sharing information between different agencies will make such social network analysis more effective; unfortunately, it may violate the privacy of some sensitive information. There is always a tradeoff between the degree of privacy and the degree of utility in information sharing. Several approaches have been proposed to resolve such dilemma in sharing data from different relational tables. There is not any work on sharing social networks from different sources and yet try to minimize the reduction on the degree of privacy. In this paper, we propose a subgraph generalization approach for information sharing and privacy protection of terrorist or criminal social networks. Our experiment shows that such approach is promising. View full abstract»

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  • A framework for privacy-preserving cluster analysis

    Page(s): 46 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (294 KB) |  | HTML iconHTML  

    Releasing person-specific data could potentially reveal sensitive information of individuals. k-anonymization is a promising privacy protection mechanism in data publishing. Though substantial research has been conducted on k-anonymization and its extensions in recent years, few of them consider releasing data for a specific purpose of data analysis. This paper presents a practical data publishing framework for determining a generalized version of data that preserves both individual privacy and information usefulness for cluster analysis. Experiments on real-life data suggest that, by focusing on preserving cluster structure in the generalization process, the cluster quality is significantly better than the cluster quality on the generalized data without such focus. The major challenge of generalizing data for cluster analysis is the lack of class labels that could be used to guide the generalization process. Our approach converts the problem into the counterpart problem for classification analysis where class labels encode the cluster structure in the data, and presents a framework to evaluate the cluster quality on the generalized data. View full abstract»

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