intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applicati...
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intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applications, through an integrated technological, organizational, and policy-based approach. This paper summarizes the broad application and policy context for this emerging field. Three detailed case studies are presented to illustrate several key ISI research areas, including cross-jurisdiction information sharing;terrorism information collection, analysis, and visualization;and "smart-border" and bioterrorism applications. A specific emphasis of this paper is to note various homeland-security-related applications that have direct relevance to transportation researchers and to advocate securityinformatics studies that tightly integrate transportation research and information technologies.
intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applicati...
详细信息
intelligence and security informatics (ISI) is an emerging field of study aimed at developing advanced information technologies, systems, algorithms, and databases for national- and homeland-security-related applications, through an integrated technological, organizational, and policy-based approach. This paper summarizes the broad application and policy context for this emerging field. Three detailed case studies are presented to illustrate several key ISI research areas, including cross-jurisdiction information sharing;terrorism information collection, analysis, and visualization;and "smart-border" and bioterrorism applications. A specific emphasis of this paper is to note various homeland-security-related applications that have direct relevance to transportation researchers and to advocate securityinformatics studies that tightly integrate transportation research and information technologies.
The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless intero...
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The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-rneasure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain. (C) 2007 Elsevier B.V. All rights reserved.
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 an...
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ISBN:
(纸本)9781424424146
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.
The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless intero...
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The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-rneasure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain. (C) 2007 Elsevier B.V. All rights reserved.
In recent years border safety has been identified as a critical part of homeland security. The Department of Homeland security searches vehicles entering the country for drugs and other contraband. Customs and Border ...
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In recent years border safety has been identified as a critical part of homeland security. The Department of Homeland security searches vehicles entering the country for drugs and other contraband. Customs and Border Protection (CBP) agents believe that such vehicles operate in groups and if the criminal links of one vehicle are known then their border crossing patterns can be used to identify other partner vehicles. We perform this association analysis by using mutual information (MI) to identify pairs of vehicles that may be involved in criminal activity. CBP agents also suggest that criminal vehicles may cross at certain times or ports to try and evade inspection. We propose to modify the MI formulation to include these heuristics by using law enforcement data from border-area jurisdictions. Statistical tests and selected cases judged by domain experts show that modified MI performs significantly better than classical MI in identifying potentially criminal vehicles. (c) 2006 Elsevier B.V. All rights reserved.
Criminal networks evolve over time with the formation and dissolution of links to survive control efforts by government authorities. Previous studies have shown that the link formation process in such networks is infl...
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ISBN:
(纸本)9781595935991
Criminal networks evolve over time with the formation and dissolution of links to survive control efforts by government authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. The findings shown in this poster can help government authorities automatically predict co-offending relationships in future crimes.
Knowledge representation based on rule + exception strategies has strong cognitive roots and can play an important role in the design and implementation of intelligent information systems, including those motivated by...
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Knowledge representation based on rule + exception strategies has strong cognitive roots and can play an important role in the design and implementation of intelligent information systems, including those motivated by homeland security-related applications. In this framework, you can succinctly describe normal situations or behaviors by easily understandable rules. You can also capture abnormal cases by exceptions to these rules. This rule + exception-based approach has great potential to evolve into a unified multiple-level data description and understanding framework applicable across many securityinformatics *** article is part of a special issue on homeland security.
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