  Law-enforcement investigations aimed at preventing attacks by violent
extremists have become increasingly important for public safety. The problem is
exacerbated by the massive data volumes that need to be scanned to identify
complex behaviors of extremists and groups. Automated tools are required to
extract information to respond queries from analysts, continually scan new
information, integrate them with past events, and then alert about emerging
threats. We address challenges in investigative pattern detection and develop
an Investigative Pattern Detection Framework for Counterterrorism (INSPECT).
The framework integrates numerous computing tools that include machine learning
techniques to identify behavioral indicators and graph pattern matching
techniques to detect risk profiles/groups. INSPECT also automates multiple
tasks for large-scale mining of detailed forensic biographies, forming
knowledge networks, and querying for behavioral indicators and radicalization
trajectories. INSPECT targets human-in-the-loop mode of investigative search
and has been validated and evaluated using an evolving dataset on domestic
jihadism.
