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 Work
Research Thrust 1: Tools for Risk
Assessment and Anomaly Detection
a. Risk Scoring of Containers
b.Visualization of Data
c. Machine Learning to Distinguish
Threat from non-Threat Radiation
Visualization of Port to Port Shipments
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Research Thrusts: Recent Work
Research 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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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
and Anomaly Detection: Recent Highlights
• Container Risk Scoring:
– 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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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment and
Anomaly Detection: Recent Highlights
• Container Risk Scoring:
– Looked at manifest data from before and after the
Japanese tsunami. Expect to find differences.
Credit: National Geographic News
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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment and
Anomaly Detection: Recent Highlights
• Container Risk Scoring:
– 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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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
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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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
and Anomaly Detection: Recent Highlights
• Visualization of Manifest Data:
– Encoded shipment information as weighted timevariant graphs amenable to fast stream processing
and visualization
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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
and Anomaly Detection: Recent Highlights
• Visualization of Manifest Data:
– Developed novel representation of manifest data
amenable to fast visualization and processing
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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
and Anomaly Detection: Recent Highlights
• Visualization of Manifest Data:
– Developed novel algorithm based on “combinatorial
discrepancy” to detect anomalous traffic in manifest
data
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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
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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Research Thrusts: Recent Work
Research Thrust 1: Tools for Risk Assessment
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 CZTbased 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 Work
Research 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 Defense
To 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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General Formulation: Outer layer(s)
plus inner layer(s) – paths of approach
Model Assumptions: First Model:
•Each incoming path u has a dangerous “flow” Fu
•At each sensor k, the probability of detection is a
function Dk(Rk) of the resources Rk allocated to
that sensor.
•Assume that Dk(Rk) is a concave, piecewise linear
function.
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General Formulation: Outer layer(s)
plus inner layer(s) – paths of approach
Model Assumptions: First Model
Special Case: The Case of Two Layers
•Assume that the outside layers share a limited
resource budget and so do the inside layers.
•More subtle models allow one to make decisions
about how much budget to allocate between
inside and outside.
•Goal: Allocate the total outside resources among
individual sensors and allocate the total inside
resources among individual sensors in order to
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maximize the illegal flow detected.
General Formulation: Outer layer(s)
plus inner layer(s) – paths of approach
Model Assumptions: First Model
Special Case: The Case of Two Layers
•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 that
we ultimately aim for to confuse the attacker.
That is an added component for future work.
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General Formulation: Outer layer(s)
plus inner layer(s) – paths of approach
Model Assumptions: First Model
Special Case: The Case of Two Layers
•Since there are only 2 layers, we can identify
the path name with the outer layer sensor where
it begins.
•Thus, path u is the path beginning at outer
sensor u.
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The Case of Two Layers
Dangerous flow captured at outside sensor j
Dangerous flow not captured
at outside sensor j that is
captured at inside sensor i
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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 a
brute 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
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the problem in our context.
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
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avoids the worst part of the computation.
Methods Solve Some Special Cases
Detection network
architecture
First assumption:
linear detection
rates both
inside and outside
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Changing the detection rate function
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•Our methods for this simple problem as well as
the 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
Case of 2
Outside sensors
(green and blue)
and 1 inside
sensor
Piecewise linear
detection rate functions
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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 with 4 inside sensors
and 2 outside sensors per inside sensor
•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.
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Case of an Adaptive Adversary
•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.
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The Problem for Two Layers with
an Adaptive Adversary
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The Case of Two Layers with
an Adaptive Adversary
•We have developed methods that work with
multiple inside sensors and multiple outside sensors
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Solution with 4 inside sensors and
2 outside sensors per inside sensor
•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.
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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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Testing Layered Defense Ideas at
NFL Stadiums
• 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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Testing Layered Defense Ideas at
NFL Stadiums
• 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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Testing Layered Defense Ideas at
NFL Stadiums
• 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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Testing Layered Defense Ideas at
NFL Stadiums
• 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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Testing Layered Defense Ideas at
NFL Stadiums
• Working with NFL stadiums
wanding
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Testing Layered Defense Ideas at
NFL Stadiums
• Also looking at doing ticket scans first – as an
extra layer of inspection
wanding
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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
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Project Team
Warren Powell
Savas Dayanik
Peter Frazier (grad student)
Ilya Rhyzov (grad student)
Kazutoshi Yamazaki (grad student)
Texas State University – San Marcos
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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
Thrust 1: Tools for threat detection and risk assessment
Isotope ID
Manifest data
Through
analysis
Machine learning
Risk scoring
for containers
Broader Impact
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Thrust 2: layered defense
Layered
defense
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

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:
PY01: $486K
 Duration: 48 months
PY02: $491K
44 months (to date)
Major Milestones / Accomplishments
PY03: $494K
PY04+05: $529K
 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
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Last updated on: 07/20/12