DANTE: Operational Studies of Technology, Tactics...
Transcript of DANTE: Operational Studies of Technology, Tactics...
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Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the
U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
DANTE: Operational Studies of Technology, Tactics, & Procedures at Soft Targets
Nerayo Teclemariam, [email protected] 17, 2017
SAND2017-12189M
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Bottom Line Up Front Context: Recent terrorist attacks in the West have shown increased interest in publically accessible locations
and “soft targets” (e.g., Brussels Airport, Istanbul Airport).
Problem: Adaptive adversaries have large and varied attack space while defensive resources (e.g., hardening measures, protective forces, detection technologies) are cost constrained; allocation of defenses requires prioritization with imperfect and uncertain information of the threat.
Solution: Technology developed for and used by the DoD and DOE physical protection missions allows decision makers to understand the potential effectiveness of current or future technology, attacker and defender tactics, or procedures for soft targets in a quantitative, multi-scenario approach.
Decision makers can rapidly explore:
1. “what-if” scenarios and explore effectiveness of current defenses to evolving portfolio of threats;
2. requirements of future technologies;
3. tactics to threat shift and drive adversaries to strengths of defense
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Method
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3D Terrain Environment
– Terrain Surface
• Openflight Terrain format
• GeoTiff Imagery
– Buildings, Barriers
• Import various geometry formats
– Fences, Roads, Water,etc…
• Import as linear data, polygonal data,
imagery
3D Terrain
Environment
Assets
Tactics
Assets
– People and Teams
– Vehicles (Mounted WeaponsDOFs)
– Weapons (Type of weapon, type of
ammo, # of rounds)
– Lethality (weapon and target data)
– Facility Sensors
Tactics
– Primary Plan which is a sequence of
actions to performmission• Examples of Actions: Move, Drive,
Patrol, PermissionToEngage, Breach, Delays, Suppress, Investigate,…
– Behaviors or Roles of People
– Secondary or Backup Plans
– Termination conditions
• Success and Failure
• Batch Statistical Analysis– Insights into “key” players
and events with statisticaldistribution
– Probability of neutralization computation
• Post Processing Output
– Data capture• XML files• Database enabled
– Graphs and Plots
• Replay Mode
– 3D Scenario Replayer
– Navigate and Query Data
Artificial Intelligence / Behavior Framework
Batch/Interactive: Monte Carlo Techniques
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Facility Model Overview - ExteriorPhysical models can be rapidly created with publically available information
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Facility Model Overview – InteriorPhysical models can be rapidly created with publically available information
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Illustrative Case Study: Explore effectiveness of security options through scenario comparisons that capture variability and uncertainty of adversaries
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Team 2
Team 1
Armed TSA
Agents
3 Disguised
Adversaries
Civilians
Team 3
Scenario #2: Active Shooter with Enhanced Security
Team 2
Team 1
Unarmed
TSA Agents
3 Disguised
Adversaries
Civilians
Scenario #1: Active Shooter Baseline
Twenty (20) simulation runs varying different locations of agents, behaviors, and adversaries
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Visualization of scenarios gives insight into how can events unfold
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Analytics allows deeper understanding of collection of complex attacker/defender factors
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Reduced
Civilian
Casualties
Reduced
Defender
Casualties
Green: Civilians
Blue: Defenders
Red: Adversaries
Nu
mb
er o
f En
tities
Nu
mb
er o
f En
tities
Time (10s)
Duration of Attack
Duration of Attack
Time (10s)
Scenario #2: EnhancedScenario #1: Baseline
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Analytics and Visualization allow deeper understanding of results
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Scenario #2: Enhanced
Adversaries Shots and Kills of Civilians and Defenders
Scenario #1: Baseline (Notional)
Adversaries moved freely in terminal in baseline, engaged by law enforcement team #1
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Visualization allow deeper understanding of results
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Defender Shots and Kills of Adversaries
Scenario #2: EnhancedScenario #1: Baseline (Notional)
Adversaries engaged rapidly by armed TSA agents and additional law enforcement team #3
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Additional threats can be considered; on-person IED Movie
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Potential Analyses/Applications
What-If Analysis Are there actions that can be taken to shift adversaries to strength of defense?
What is the marginal benefit for each additional armed guard or technology added?
How do security metrics change if additional hardening structures were deployed?
How does security change if adversary shifts tactics, technology type, or behaviors?
How do the various security layers interact with each other?
Technology Requirements What is the trade-space of equivalent performance for technology performance parameters
(e.g., sensitivity, speed,..) and operational deployment factors (e.g., # of units, locations,…)?
Where would a stand-off explosives detector best be located? Where is it acceptable?
Exercises and ConOps Valuation of scenarios can support development of policy and CONOPS and act as documentation and
supporting justification for decisions.
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Requirements for Adaption to Aviation Security
The Dante capability was developed for DoD/DOE applications around nuclear physical security, and there would need to be some adaptation of the capability to apply to new domains.
In particular, the model would have to be include More insight into civilian behaviors, as most current behaviors are based on DoD actions.
More insight into law enforcement response/detain/confrontation behaviors. In demo, responders are currently shooting to recognition of threat.
Inclusion of more civilian detection and defense technologies.
Understanding of key metrics for evaluation, which might require development of additional backend tools.
13http://umbra.sandia.gov/
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Nerayo Teclemariam, Ph.DDHS & Defense SystemsSandia National Laboratories
http://umbra.sandia.gov/
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Method
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3D Terrain
Environment
Assets(people, equipment,
sensors)
Tactics
• Security Metrics
(Graphs and Plots)
• Batch Statistical
Analysis
• Replay Mode via
Visualization
Artificial Intelligence / Behavior Framework
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Scenario #1: Baseline (Notional)
Threat: Three (3) Active Shooters
Defense: Two (2) law enforcement teams and unarmed TSA
Analysis: Twenty (20) simulation runs varying different locations of agents, behaviors, and adversaries
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Team 2
Team 1
Unarmed
TSA Agents
3 Disguised
Adversaries
Civilians
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Scenario #2: Additional Response Teams/Armed TSA Agents
Threat: Three (3) Active Shooters
Defense: Three (3) law enforcement teams and armed TSA
Change Over Baseline: Additional law enforcement team (e.g., Team 3) and armed TSA
Analysis: Twenty (20) simulation runs varying different locations of agents, behaviors, and adversaries
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Team 2
Team 1
Armed TSA
Agents
3 Disguised
Adversaries
Civilians
Team 3
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Visualization allow deeper understanding of results
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Defender Shots and Kills of
Adversaries
Adversaries Shots and Kills of Civilians
and DefendersAdversary Movements
Scenario #1: Baseline (Notional); Summary of ALL 20 Simulation Conditions
Adversaries moved freely in terminal, engaged by law enforcement team #1
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Analytics and Visualization allow deeper understanding of results
Adversaries engaged rapidly by armed TSA agents and additional law enforcement team #319
Defender Shots and Kills of
Adversaries
Adversaries Shots and Kills of Civilians
and DefendersAdversary Movements
Scenario #2: Additional Response Teams/Armed TSA Agents; Summary of ALL 20 Simulation Conditions
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Threat Shifting: Attacks on Soft Targets/Areas Attractive
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22 March 2016, Brussels Airport
20 March 2015, New Orleans Airport
28 June 2016, Istanbul Airport
4 July 2002, Los Angeles Int’l Airport
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Reagan National Airport (DCA)
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