Secure interoperation for effective data mining in border control and homeland security applications

ABSTRACT

Our NSF funded project aims at providing decision makers with the ability to extract and fuse information from multiple, hetertgeneous sources in response to a query while operating under a decentralized security administration. Our motivation comes from US Customs, which embarked on a major modernization initiative of its Information Technology systems. Drawing in data from Customs trade systems, targeting inspectors review manifest information as well as strategic and tactical intelligence to determine "high-risk" shipments and containers. This entails a considerable level of communication and data sharing between various government agencies. Based on the idea of "Smart Borders", the system will utilize data available from different agencies, ports and customs divisions to supplement the profiling by targeting towards anomalies, and detect various flags raised by non-conforming shipments or abnormal behavior of inbound cargos and raise a combination of alerts. The output of this project would ideally enhance the security aspect of the Automated Commercial Environment (ACE) system by incorporating the concept of semantic interoperability, anomaly detection and subsequent spatial and geographical visualization of information that can help Customs inspectors make better decisions.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.1 Project and People Management
          Subjects: Systems analysis and design
      K.6.5 Security and Protection (D.4.6, K.4.2)

General Terms:
Design, Management, Security