sensor management problems of nuclear detection – layered defense

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Sensor Management Problems of Nuclear Detection – Layered Defense

Fred S. Roberts

Rutgers University

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Multi-disciplinary, Multi-institutional Project

•Based at Rutgers University•Partners at Princeton, Texas State University – San Marcos•Collaborators at LANL, PNNL, Sandia

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Much of this work takes place at CCICADA

Founded 2009 as a DHS University Center of Excellence – the DHS CCI COE based at Rutgers

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Key Underlying Project Themes

•New developments in hardware are important in nuclear detection/prevention, but so are new algorithms, models, and statistical methods •Nuclear detection/prevention involves sorting through massive amounts of information•We need ways to make use of as many sources of information as possible.

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Research Thrusts: Recent Work

1. Tools for Risk Assessment and Anomaly Detection

2. Layered Defense

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Research Thrusts: Recent WorkResearch Thrust 1: Tools for Risk

Assessment and Anomaly Detectiona. Risk Scoring of Containersb.Visualization of Datac. Machine Learning to Distinguish

Threat from non-Threat Radiation

Visualization of Port to Port Shipments

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Research Thrusts: Recent WorkResearch Thrust 1: Tools for Risk Assessment

and Anomaly Detection: Recent Highlights• Container Risk Scoring:

– We looked at a year’s worth of manifest data from container ships – every Wed.

– Goal: Identify mislabeled or anomalous shipments through scrutiny of a manifest data

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– Used our penalized regression scoring to identify risk scores and patterns or time trends in variables.

– Emphasis on relationships among container shipment contents, port of origin and destination, carrier, etc.

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– Looked at manifest data from before and after the Japanese tsunami. Expect to find differences.

Credit: National Geographic News

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– Looked at manifest data from before and after the Japanese tsunami. Expect to find differences.

– Found that pattern of frequency data based on “domestic port of unlading” is statistically different before and after the tsunami.

– But the pattern based on distribution of carrier is not– Conclusion: Don’t depend on just one variable to

uncover anomalies.

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and Anomaly Detection: Recent Highlights• Visualization of Manifest Data:

– Data visualization is a powerful new area of research enabling rapid insight into patterns and departures from patterns

– Analyzed relationships among container shipment contents, foreign port of origin and US destination port

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– Encoded shipment information as weighted time-variant graphs amenable to fast stream processing and visualization

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– Developed novel representation of manifest data amenable to fast visualization and processing

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– Developed novel algorithm based on “combinatorial discrepancy” to detect anomalous traffic in manifest data

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and Anomaly Detection: Recent Highlights• Machine Learning to Distinguish Threat from

non-Threat Radiation– Goal: distinguish non-threat sources of radiation

from threat materials and identify an isotope.– Compared machine learning Topic Modeling

algorithms: recently-popularized Higher Order Latent Dirichlet Allocation (H0-LDA) vs. traditional LDA.

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and Anomaly Detection: Recent Highlights• Machine Learning to Distinguish Threat from

non-Threat Radiation– Learning based on data set of 302 spectra including

17 isotopes and background.– Analyze gamma-ray spectra generated by CZT-

based handheld detectors– Comparing HO-LDA to traditional LDA.– Concentrated on GA67, I131, In111, Tc99m– HO-LDA performed statistically significantly better

than LDA

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Research Thrusts: Recent WorkResearch Thrust 2: Layered Defense

Target

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Research Thrust 2: Layered Defense

• We have formulated a model of how to locate nuclear surveillance in the area around a facility, e.g., roadways and walkways approaching sports stadiums.

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Layered Defense• This relates to a CCICADA

project in connection with the National Football League.

• Developing simulation models for evacuation of stadiums.

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Layered DefenseTo develop our ideas, we have formulated a model of a “perimeter” defense of the target with several layers of defense:

•Limited budget for surveillance•How much to invest in each layer?•Defense at outer layers might be less successful but could provide useful information to selectively refine and adapt strategies at inner layers.•Arranging defense in layers so decisions can be made sequentially might significantly reduce costs and increase chance of success.

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Layered Defense

Abstract model of layered defense:

• Target in middle• Threats arrive via 4

inner channels• Each combines 2 outer

outer flows of vehicles, persons, etc.

Target

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Layered Defense

Abstract model of layered defense:

• Fixed budget for outer layer and for inner layer defense

• Can choose among detectors with different characteristics and costs

• How optimize probability of detection?

Target

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Layered Defense

Different models for:• Flow along different

paths• Prob. of detection at

different locations (outer, inner)

• Allowable modifications of inner defense strategies based on outer layer results

Target

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Layered Defense• Monitoring at outer layer not only hinders an

attacker but can provide information about current state of threat that can be used to refine and adapt strategies at inner layers.

• There is a complex tradeoff between maximizing the cost-effectiveness of each layer and overall benefits from devoting some efforts at the outer layer to gathering as much information as possible to maximize effectiveness of the inner layer.

• We have formulated this as an optimization problem.

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General Formulation: Outer layer(s) plus inner layer(s) – paths of approach

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Model Assumptions: First Model:

•Each incoming path u has a dangerous “flow” Fu

•At each sensor k, the probability of detection is afunction Dk(Rk) of the resources Rk allocated to that sensor. •Assume that Dk(Rk) is a concave, piecewise linearfunction.

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Model Assumptions: First ModelSpecial Case: The Case of Two Layers

•Assume that the outside layers share a limitedresource budget and so do the inside layers.•More subtle models allow one to make decisionsabout how much budget to allocate between inside and outside.•Goal: Allocate the total outside resources amongindividual sensors and allocate the total inside resources among individual sensors in order tomaximize the illegal flow detected.

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•Goal: Allocate the total outside resources among individual sensors and allocate the total inside resources among individual sensors in order to maximize the illegal flow detected.•Note: So far, this model does not have the random allocation of resources to sensors thatwe ultimately aim for to confuse the attacker. That is an added component for future work.

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•Since there are only 2 layers, we can identifythe path name with the outer layer sensor whereit begins. •Thus, path u is the path beginning at outersensor u.

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Dangerous flow captured at outside sensor j

Dangerous flow not captured at outside sensor j that is captured at inside sensor i

The Case of Two Layers

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Solving the Optimization Problem•This formulates the problem as a non-linear optimization problem.•A standard approach to such problems is abrute force approach that fixes a resource “mesh”size and enumerates all possibilities.

– Discretize the resource space for each sensor into subintervals– Examine every possible resource allocation

•That approach is not computationally feasible for the problem as we have formulated it.•We have developed a new approach to solving the problem in our context.

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Solving the Optimization Problem•We have developed a new approach to solving the problem in our context.•Still discretize the resource space for interior sensors into subintervals and solve that.•However, we can now find the optimal configuration for the exterior sensors by solving a linear programming problem for each combination of interior and exterior sensors.•An improvement, but this is still too computationally intensive.•However, a dynamic programming variant avoids the worst part of the computation.

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Methods Solve Some Special Cases

Detection networkarchitecture

First assumption: linear detection rates bothinside and outside

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Changing the detection rate function

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•Our methods for this simple problem as well asthe more complex problems we will describe were applied on a simple AMD Phenom X4 9550 workstation with 6GB of DDR2 RAM, and were often solved in a matter of seconds.

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A more complicated network: Multiple outside sensors

Piecewise lineardetection rate functions

Case of 2Outside sensors (green and blue) and 1 inside sensor

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A more complicated network: Multiple outside and multiple

inside sensors

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•Our methods generalize to this case.•Even with 4 inside sensors and 2 outside sensors per inside sensor, solution in < 2 minutes on modest workstation.

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•Solution “tableau” includes10,302 distinct points.•Solution in < 2 minutes on modest workstation.•Methods feasible up to 10 inside sensors.•After that, need approximation methods.

Solution with 4 inside sensorsand 2 outside sensors per inside sensor

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•So far, our model assumed a fixed flow of dangerous material on each pathway.•What if we have an adaptive adversary who recognizes how much of a resource we use for sensors on each node and then chooses the path that minimizes the probability of detection?•To defend against such an adversary we might seek to assign sensor resources so as to maximize the minimum detection rate on any path.

Case of an Adaptive Adversary

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The Problem for Two Layers with an Adaptive Adversary

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•We have developed methods that work withmultiple inside sensors and multiple outside sensors

The Case of Two Layers with an Adaptive Adversary

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•Solution “tableau” had 40,401 distinct points.•Solution in 3102 seconds (52 minutes) on modest workstation.•Hope to be able to speed up so methods feasible for up to 10 inside sensors.•After that, need approximation methods.

Solution with 4 inside sensors and2 outside sensors per inside sensor

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Testing Layered Defense Ideas at NFL Stadiums

• Working with NFL stadiums• Looking at variety of inspection problems, not

just nuclear detection.• Gathering data about how they do layered

defense and building simulation models

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• Model for inspection:– Assume all basic inspection methods perform like

M/M/1 queues (inter-arrival times and service times are exponentially distributed)

– Studying a variety of different kinds of inspections– Five measures of effectiveness:

• Detection rate• False alarm rate• Monetary cost• Throughput• Average waiting time

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• Model for inspection:– Comparing different kinds of strategies

• Mixed strategy: Execute inspection strategy Ai

on fraction xi of people• Layered strategy: Execute strategy A for

everyone; then strategy B on those who test positive and strategy C on those who test negative

• Distributed strategy: Split the current queue for strategy A into a k-multiserver queue for strategy A

• Randomization strategy: if you can’t inspect everyone.

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• Model for inspection:– For layered strategies:– Have developed an algorithm for finding the

convex hull of “dominating strategies that:Satisfy some conditions such as maximize

detection rate and minimize false alarm rate and monetary cost

subject to constraints on maximum cost and minimum throughput.

– Algorithm runs in a few seconds if maximum 2 layers, takes 30 minutes for 3 layers.

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• In practice: Looking at three types of inspection:– Wanding– Pat-down– Bag inspection

• Observing stadium inspections and gathering data about each type of inspection, in particular length of time it takes.

• Data shows major differences depending on inspector, time before kickoff, etc.

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• Working with NFL stadiums

wanding

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• Also looking at doing ticket scans first – as an extra layer of inspection

wanding

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Project Team• Rutgers University

– Fred Roberts– James Abello– Tsvetan Asamov (grad student)– Endre Boros– MIkey Chen (undegrad)– Jerry Cheng (grad student)– Sid Dalal (RAND Corp, consultant)– Robert Davis (undergrad student)– Emilie Hogan (grad student)– Christopher Janneck (grad student)– Paul Kantor– Adam Marszalek (grad student)– Dimitris Metaxas– Christie Nelson (grad student)– Alantha Newman (postdoc)– Neel Parikh (undergrad)– Jason Perry (grad student)– Bill Pottenger– Brian Thompson (grad student)– Minge Xie– Emre Yamangil (grad student)– Stavros Zinonos (grad student)

• Princeton University– Warren Powell– Savas Dayanik– Peter Frazier (grad student)– Ilya Rhyzov (grad student)– Kazutoshi Yamazaki (grad student)

• Texas State University – San Marcos– Nate Dean– Jill Cochran (grad student)

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Project Team: National Lab Partners(helping with advice, information, data)

• PNNL– Terence Critchlow

– James Ely

– Cliff Joslyn

• LANL– Frank Alexander

– Nick Hengartner

• Sandia– Jon Berry

– Bill Hart

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Thank you

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Title: Sensor Management Problems of Nuclear Detection Org/PI: Rutgers University / Fred S. Roberts

Technical Merit

Our project focuses on managing and mitigating uncertainty for improved collection and interpretation of sensor data while exploiting randomness for unpredictable surveillance

Classification methods tailored to radiation sensor data can reduce nuisance alarms; new methods for analyzing manifest data lead to anomaly detection and risk scoring; layered surveillance to thwart adversaries

Technical Approach

Risk scoring methods; visualization for anomaly detection; machine learning for isotope identification; optimization and simulation for layered defense

Broader Impact

Students supported: postdoc (1); graduate (10); undergrad (3) Part of (3) PhD dissertations to date; (3) more nearing completion More than (10) additional graduate students participating Developed new undergraduate course on “Optimal Learning” at

Princeton University, with related textbook in progress Held workshop involving five projects in the DNDO program +

Fall 2010 workshop on adversarial decision making Enhanced relations w/ national labs, incl. (2) summer internships Many project methods apply to other fields: e.g. machine learning

methods are being applied for police force deployment; layered defense to NFL games

(30) papers published/accepted; (11) more under review

Schedule/Cost: Duration: 48 months 44 months (to date) Major Milestones / Accomplishments

Developed machine learning tools for risk scoring and isotope classification, esp. higher-order methods and preprocessing tools; new statistical methods for risk scoring & new split & conquer algorithm for larger data sets; visualizations to observe patterns in manifest data rapidly pinpoint anomalies; novel models of layered defense

Team

Co-PI: Warren Powell, Princeton University Collaborating Universities: Princeton University; Texas State

University – San Marcos National Labs interaction: PNNL; LANL; Sandia; LLNL

Last updated on: 07/20/12

PY01: $486K PY03: $494K

PY02: $491K PY04+05: $529K

Thrust 1: Tools for threat detection and risk assessment

Manifest dataanalysis

Risk scoringfor containers

Isotope IDThroughMachine learning

Thrust 2: layered defense

Layereddefense

sensor management problems of nuclear detection – layered defense

Documents

recent workresearch

risk assessment

recent worktools

risk scores

manifest dataresearch

anomaly detection2

port shipmentsresearch

visualizationresearch