Toward Adaptive, Risk-Informed
Allocation of Border Security Assets
Joel Predd and Henry Willis
February 26, 2009
RAND Research on Counter-IED Operations in
Iraq Illustrates Benefits of Tools
• Problem: Ground forces in Iraq had
limited resources for counter-IED
operations
• Method: RAND developed methods
and tools to predict location and
time of future IED threats based on
database of recent attacks
• Application: Threat predictions
helped brigades decide where to
direct surveillance
W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.
2
The Problem Concerns Operational
Resource Allocation
U.S. law enforcement
agencies need to
direct limited border
resources to detect
and identify risks
along the border
3
This Problem Statement Includes Four Key
Terms That Need to be Further Defined
U.S. law enforcement
agencies need to
direct limited border
resources to detect
and identify risks
along the border
• Resources include both
technology and people
• Focus on resources that
detect and identify, enable
engagement and resolution
• Potential risks include both
smuggling and border crossing
• Southwestern land border is
the near-term focus, plan for
extensions to North
4
Study Objective
To develop and
evaluate machine
learning-based
methods and tools to
facilitate adaptive,
data-driven, riskbased allocation of
border security
resources
5
Four Principles Guide The Study Objective
•
To develop and
evaluate machine
learning-based
methods and tools to •
facilitate adaptive,
data-driven, riskbased allocation of
border security
resources
Machine learning refers to a set
of statistical and computational
methods
Method should
– be adaptive, because border
crossers are
– be informed by data
– incorporate border threats,
vulnerabilities and
consequences (i.e., risk)
6
Example 1: Allocating Counter-IED
Surveillance Assets
• Problem: Ground forces in Iraq had
limited resources for counter-IED
operations
• Method: RAND developed methods
and tools to predict location and
time of future IED threats based on
database of recent attacks
• Application: Threat predictions
helped brigades decide where to
direct surveillance
W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.
7
Example 2: A Meta-Allocation of Problem of
Choosing Predictive Tools
• (Meta-)Problem: Ground forces in Iraq had to choose one of
multiple predictive tools
– Each tool was itself designed to facilitate surveillance
resource allocation, and better in different circumstances
• Method: RAND developed online learning methods to
adaptively aggregate suite of tools based on historical
performance
• Application: Aggregate tools could support original
surveillance asset allocation problems
W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.
8
Example 3: Research at USC CREATE
Provides Another Illustration
• Problem: Airport security has limited resources to
allocate to checkpoints and canine patrols
• Method: Researchers at USC CREATE developed
methods and tools to systematically schedule
checkpoints and canine patrols based on theory of
Bayesian Stackelberg games
• Application: Software tool called ARMOR is used to
schedule canine patrols
Pita, J., Jain, M., Western, C., Paruchuri, P., Marecki, J., Tambe, M., Ordonez, F., Kraus, S., Deployed ARMOR, "Protection: The
Application of a Game Theoretic Model for Security at the Los Angeles International Airport," in Proceedings of the Seventh International
Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Industry Track), 2008
9
We Are Working to Leverage This
Research to Benefit CBP Operations
• Limited resources require tactical decisions about
how to allocate
– Ground sensors
– Patrols
– UAVs
– Detection
–…
• How to do so in way the adaptively integrates
tactical data about threats, vulnerabilities and
consequences?
10
The Product: A Tool To Help Sector Chiefs
Deploy Sensors and Patrols According to Risk
• The tool will identify future risks by making
predictions from historical data
• Threat data
– E.g., data may include a record of the location and time of past
detections or interdictions
• Vulnerability data
– E.g., GIS data about cross-border roads or paths, sector
boundaries
– E.g., GIS data about topography and weather
– E.g., Location and time records of previous border security
operations, sensor deployments, and patrols
• Consequence data
– E.g, information on mission-types
11
Methodology and Work Plan
Year 1: Understand border operations, environment,
and available intelligence data and collection assets
• Plans to visit San Diego Sector
- Operation Red Zone
- Border Intelligence Center
- Air and Marine Operations Center
• Plans to visit Rio Grande Valley Sector
Year 2: Evaluate machine learning-based methods in
a simulated environment
Year 3: Explore with CBP interest in conducting field
evaluation of prototype tools
12
Summary
• A project funded through the National Center for
Border Security and Immigration
• The objective is to develop and evaluate predictive
methods and tools to facilitate adaptive, data-driven
and risk-based allocation of CBP assets
• The outcome will be that Office of Border Patrol and
the Secure Border Initiative program office will have
methods and tool to dynamically allocate assets in the
tactical environment
13
The Tool Automatically Identified Actionable Hot
Spots of Enemy Activity
• Hot spot – an area
consistently and
recently targeted by
enemy forces
• Actionable hot spot
– a hotspot where
limited surveillance
resources can be
focused
Past IED event
Road
15
The Tool Automatically Identified Actionable
Hot Spots of Enemy Activity
• Hot spot – an area
consistently and
recently targeted by
enemy forces
• Actionable hot spot
– a hotspot where
limited surveillance
resources can be
focused
5 miles
Hot spots
16
The Tool Automatically Identified Actionable
Hot Spots of Enemy Activity
• Hot spot – an area
500 meters
consistently and
recently targeted by
enemy forces
• Actionable hot spot
– a hotspot where
limited surveillance
resources can be
focused
Actionable
Hot spots
17
The Tool Automatically Identified Actionable
Hot Spots of Enemy Activity
• Hot spot – an area
500 meters
consistently and
recently targeted by
enemy forces
• Actionable hot spot
– a hotspot where
limited surveillance
resources can be
focused
Highest ranking
actionable hotspots
were candidates
for surveillance
18
NC-BSI: Adaptive, Risk-Informed Resource Allocation
Methodology
Problem: CBP and local law enforcement need
to direct limited border resources to where
they can most effectively detect and identify
risks along the border
Objective: To develop and evaluate machine
learning-based methods and tools to facilitate
adaptive, data-driven and risk-based allocation
of CBP resources
Benefits to DHS
The Office of Border Patrol and the Secure
Border Initiative program office will have tools
to dynamically allocate assets in the tactical
environment
Phase 1: Field studies to CBP sites to understand
border operations, environment, and available
intelligence data and collection assets
Phase 2: Develop machine learning-based methods
and prototype tools, and evaluate them in a
simulated environment
Phase 3: Field studies to deploy prototype tools
Deliverables and Timelines
Q1, Q2, Q3 : Visit DHS, CBP Sites; review literature;
Q4: Document findings
Year 1 Deliverables: Inventory of available
intelligence assets; assessment of available data via
whitepaper
Year 2 Deliverables: Method, prototype tool, and
evaluation
19
Year 3 Deliverables: Assessment of field studies
NC-BSI: Adaptive, Risk-Informed Resource Allocation
Elevator speech
To manage the risk of illegal border crossings
and smuggling, CBP must answer two resource
allocation questions: Where and when should
we conduct surveillance? Given the adaptive
behavior of border crossers answering these
questions requires an adaptive, data driven
approach. This project will develop and
evaluate such an approach.
Ongoing/leveraged research
Costs and Special Equipment
Investigators
Year 1: $77,250
Year 2: $87,300
Year 3: $90,000
Henry H. Willis, Ph.D.
Joel Predd, Ph.D.
JIEDDO-funded RAND IED research
– Tactical support
– Analysis of Alternatives
Risk analysis work with USC-CREATE
– ARMOR and Border Risk Model
20
RAND Analysis Uses Models and Simulations
To Support Operational Integration
Field (Live)
M&S
Virtual
M&S
Iterative
process
Computational
Models
Constructive
M&S
21
We Are Seeking Guidance on Three Topics
• What operational constraints must we take into account
– Visit border sites
• Operation REDZONE, JTF-North Campaign Planning Workshop,
El Paso Information Center, Air and Marine Operations Center
– Discuss CBP operations at sectors
• Recommendations related to scope of focus
– Which sector(s) or station(s) to visit?
– Which tactical operations might benefit most?
– Who to meet? Where to visit?
• What sample data is available?
–
–
–
–
Location and time of past detections, interdictions
Location and time of past operations, sensor deployments, and patrols
GIS data about border roads, paths, topography, weather, etc.
After Action Reviews (AARs)
22
Study Plan is to Build Tools That Integrate
With Current Practices
• We have learned that sectors
may use different methods,
and possibly share data and
lessons learned
• Southwest sectors have
employed some predictive
methods for resource
allocation
• Data about the location and
time of some border activities
are archived, shared
Source: Operation Gulf Watch
Provided By: PAIC Mark Butler, Fort Brown Station,
RGV Sector
Provided To: MAJ Eloy Cuevas, JTF-N Intelligence
Planner Date: February 2006
23
RAND Research on Counter-IED Operations in
Iraq Illustrates Benefits of Tools (Example 2)
• Problem: Intelligence had developed many predictive
tools, but had difficult choosing which heuristic to use for
resource allocation
• Method: RAND developed methods to adaptively
aggregate large suites of predictive tools using online
learning
• Application: The aggregate tool provided a way to make
a useful tool out of many
W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.
24
Example 1: Allocating Counter-IED
Surveillance Assets (2/3)
• Hot spot – an area
consistently and
recently targeted by
enemy forces
5 miles
Hot spots
25
Example 1: Allocating Counter-IED
Surveillance Assets (3/3)
• Hot spot – an area
500 meters
consistently and
recently targeted by
enemy forces
• Actionable hot spot
– a hotspot where
limited surveillance
resources can be
focused
Actionable
Hot spots
26
Example 1: Allocating Counter-IED
Surveillance Assets (3/3)
• Hot spot – an area
500 meters
consistently and
recently targeted by
enemy forces
• Actionable hot spot
– a hotspot where
limited surveillance
resources can be
focused
Highest ranking
actionable hotspots
were candidates
for surveillance
27
Example 1: Allocating Counter-IED
Surveillance Assets (3/3)
• Hot spot – an area
500 meters
consistently and
recently targeted by
enemy
The forces
main success
of this research
was
the integration
of predictive methods
• Actionable
hot spot
Highest ranking
with where
operational constraints
actionable hotspots
– a hotspot
limited surveillance
resources can be
focused
were candidates
for surveillance
28
Example 2: A Meta-Allocation of Problem of
Choosing Predictive Tools (2/3)
• Predictive heuristics admitted essentially no
theoretical analysis of effectiveness.
• Existing empirical analyses are optimistic; the results
generalize only if the methods are not actually used in
the field.
– in practice, enemy reacts to allocation methods use of
a method; existing data does not reflect adaptation
• Long-term trends and normal reactive behaviors can
go undetected.
location
…
29
time
Example 2: A Meta-Allocation of Problem of
Choosing Predictive Tools (3/3)
• RAND developed online
learning algorithms to
adaptively aggregate a
suite predictive tools
• Algorithms have
provable performance
guarantees
• Laboratory experiments
suggest competitive to
rival methods
Day
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