Research Summaries

Back A Sequential Pattern Detection Model for Predicting IED Attacks

Fiscal Year 2008
Division Research & Sponsored Programs
Department National Security Institute
Investigator(s) Kamel, Magdi N.
Sponsor Joint Improvised Explosive Device Defeat Organization (Joint)
Summary The focus of this research is the use of sequential pattern detection approaches of data mining to develop a predictive model for the timing and frequency of IED attacks. Using the CARMA association rules algorithm on historical data of religious, political, and IED attacks events, a model will be developed to explore commonly occurring or high confidence sequences of events leading to an insurgency IED attack and predict events that are likely to occur given the sequence observed to date. The expectation is that the identified sequences could be used to help plan troop movements, associated troop levels, and allocating limited resources to address imminent threats. The result of the research will be directly actionable by providing the commanders on the ground with predictive information on the likelihood of IED attack based on historical patterns of previous attacks.
Keywords
Publications Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal