02250nas a2200193   4500008004100000245009000041210006900131260000900200300001200209490000700221520168100228653001501909653000801924100002001932700001701952700002001969700001901989856004802008       2009                        eng d00aTopological Analysis of Criminal Activity Networks: Enhancing Transportation Security0 aTopological Analysis of Criminal Activity Networks Enhancing Tra  c2009  a83 - 910 v103 aThe 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.10aAccounting10aBIS1 aKaza, Siddharth1 aXu, Jennifer1 aMarshall, Byron1 aChen, Hsinchun  uhttp://dx.doi.org/10.1109/TITS.2008.201169502064nas a2200181   4500008004100000245008400041210006900125260000900194300001400203490000700217520147700224653001501701653000801716100002001724700001901744700002001763856009901783       2008                        eng d00aUsing Importance Flooding to Identify Interesting Networks of Criminal Activity0 aUsing Importance Flooding to Identify Interesting Networks of Cr  c2008  a2099-21140 v593 aCross-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.10aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun1 aKaza, Siddharth  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_JASIST_ImportanceFlooding_PrePrint.pdf