Toward Adaptive, Risk-Informed Allocation of Border Security Assets. Joel Predd and Henry Willis February 26, 2009

Transcription

1 Toward Adaptive, Risk-Informed Allocation of Border Security Assets Joel Predd and Henry Willis February 26, 2009

2 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,

3 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

4 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

5 To develop and evaluate machine learning-based methods and tools to facilitate adaptive, data-driven, riskbased allocation of border security resources Study Objective 5

6 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

7 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,

8 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,

9 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),

10 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

11 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

12 Methodology and Work Plan Year 1: Understand border opera-ons, environment, and available intelligence data and collec-on 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 conduc-ng field evalua-on of prototype tools 12

13 Summary A project funded through the Na-onal Center for Border Security and Immigra-on The objec-ve is to develop and evaluate predic-ve methods and tools to facilitate adap-ve, data driven and risk based alloca-on of CBP assets The outcome will be that Office of Border Patrol and the Secure Border Ini-a-ve program office will have methods and tool to dynamically allocate assets in the tac-cal environment 13

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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 Past IED event Road 15

16 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

17 The Tool Automatically Identified Actionable Hot Spots of Enemy Activity Hot spot an area consistently and recently targeted by enemy forces 500 meters Actionable hot spot a hotspot where limited surveillance resources can be focused Actionable Hot spots 17

18 The Tool Automatically Identified Actionable Hot Spots of Enemy Activity Hot spot an area consistently and recently targeted by enemy forces 500 meters Actionable hot spot a hotspot where limited surveillance resources can be focused Highest ranking actionable hotspots were candidates for surveillance 18

19 NC BSI: Adap-ve, Risk Informed Resource Alloca-on Problem: CBP and local law enforcement need to direct limited border resources to where they can most effec-vely detect and iden-fy risks along the border Objec2ve: To develop and evaluate machine learning based methods and tools to facilitate adap-ve, data driven and risk based alloca-on of CBP resources Methodology Phase 1: Field studies to CBP sites to understand border opera-ons, environment, and available intelligence data and collec-on 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 Benefits to DHS The Office of Border Patrol and the Secure Border Ini-a-ve program office will have tools to dynamically allocate assets in the tac-cal environment 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 evalua-on Year 3 Deliverables: Assessment of field studies 19

20 NC BSI: Adap-ve, Risk Informed Resource Alloca-on Elevator speech To manage the risk of illegal border crossings and smuggling, CBP must answer two resource alloca-on ques-ons: Where and when should we conduct surveillance? Given the adap-ve behavior of border crossers answering these ques-ons requires an adap-ve, data driven approach. This project will develop and evaluate such an approach. Costs and Special Equipment Year 1: $77,250 Year 2: $87,300 Year 3: $90,000 Ongoing/leveraged research JIEDDO funded RAND IED research Tac-cal support Analysis of Alterna-ves Risk analysis work with USC CREATE ARMOR and Border Risk Model Inves2gators Henry H. Willis, Ph.D. Joel Predd, Ph.D. 20

21 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

22 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

23 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

24 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,

25 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

26 Example 1: Allocating Counter-IED Surveillance Assets (3/3) Hot spot an area consistently and recently targeted by enemy forces 500 meters Actionable hot spot a hotspot where limited surveillance resources can be focused Actionable Hot spots 26

27 Example 1: Allocating Counter-IED Surveillance Assets (3/3) Hot spot an area consistently and recently targeted by enemy forces 500 meters Actionable hot spot a hotspot where limited surveillance resources can be focused Highest ranking actionable hotspots were candidates for surveillance 27

28 Example 1: Allocating Counter-IED Surveillance Assets (3/3) Hot spot an area consistently and recently targeted by enemy forces Actionable hot spot a hotspot where limited surveillance resources can be focused 500 meters The main success of this research was the integration of predictive methods with operational constraints Highest ranking actionable hotspots were candidates for surveillance 28

29 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 time 29

30 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 Cumulative loss Day 30