TY - JOUR T1 - Topological Analysis of Criminal Activity Networks: Enhancing Transportation Security JF - IEEE Transactions on Intelligent Transportation Systems Y1 - 2009 A1 - Kaza,Siddharth A1 - Xu,Jennifer A1 - Marshall,Byron A1 - Chen,Hsinchun KW - Accounting KW - BIS AB - The security of border and transportation systems is a critical component of the national strategy for homeland security. The security concerns at the border are not independent of law enforcement in border-area jurisdictions because the information known by local law enforcement agencies may provide valuable leads that are useful for securing the border and transportation infrastructure. The combined analysis of law enforcement information and data generated by vehicle license plate readers at international borders can be used to identify suspicious vehicles and people at ports of entry. This not only generates better quality leads for border protection agents but may also serve to reduce wait times for commerce, vehicles, and people as they cross the border. This paper explores the use of criminal activity networks (CANs) to analyze information from law enforcement and other sources to provide value for transportation and border security. We analyze the topological characteristics of CAN of individuals and vehicles in a multiple jurisdiction scenario. The advantages of exploring the relationships of individuals and vehicles are shown. We find that large narcotic networks are small world with short average path lengths ranging from 4.5 to 8.5 and have scale-free degree distributions with power law exponents of 0.85–1.3. In addition, we find that utilizing information from multiple jurisdictions provides higher quality leads by reducing the average shortest-path lengths. The inclusion of vehicular relationships and border-crossing information generates more investigative leads that can aid in securing the border and transportation infrastructure. VL - 10 UR - http://dx.doi.org/10.1109/TITS.2008.2011695 CP - 1 U2 - a U4 - 2609299457 ID - 2609299457 ER - TY - JOUR T1 - Using Importance Flooding to Identify Interesting Networks of Criminal Activity JF - Journal of the Association for Information Science and Technology Y1 - 2008 A1 - Marshall,Byron A1 - Chen,Hsinchun A1 - Kaza,Siddharth KW - Accounting KW - BIS AB - Cross-jurisdictional law enforcement data sharing and analysis is of vital importance because law breakers regularly operate in multiple jurisdictions. Agencies continue to invest massive resources in various sharing initiatives despite several high-profile failures. Key difficulties include: privacy concerns, administrative issues, differences in data representation, and a need for better analysis tools. This work presents a methodology for sharing and analyzing investigation-relevant data and is potentially useful across large cross-jurisdictional data sets. The approach promises to allow crime analysts to use their time more effectively when creating link charts and performing similar analysis tasks. Many potential privacy and security pitfalls are avoided by reducing shared data requirements to labeled relationships between entities. Our importance flooding algorithm helps extract interesting networks of relationships from existing law enforcement records using user-controlled investigation heuristics, spreading activation, and path-based interestingness rules. In our experiments, several variations of the importance flooding approach outperformed relationship-weight-only methods in matching expert-selected associations. We find that accuracy in not substantially affected by reasonable variations in algorithm parameters and demonstrate that user feedback and additional, case-specific information can be usefully added to the computational model. VL - 59 UR - http://people.oregonstate.edu/~marshaby/Papers/Marshall_JASIST_ImportanceFlooding_PrePrint.pdf CP - 13 U2 - a U4 - 2609309697 ID - 2609309697 ER -