Date post: | 17-Dec-2015 |
Category: |
Documents |
Upload: | madeline-shields |
View: | 216 times |
Download: | 1 times |
Security via Strategic Randomization
Milind Tambe Fernando OrdonezPraveen ParuchuriSarit Kraus (Bar Ilan, Israel)Jonathan Pearce, Jansuz MareckiJames Pita, Christopher Portway
University of Southern CaliforniaLos AngelesNovember, 2007
Objective: Guarantee Randomness of Security Processes While Meeting Security Quality Requirements
• Limited /uncertain knowledge of opponent(s)• Opponent monitors defenses, exploits patterns• Examples: Aerial surveillance, patrolling,…
Research Problem Definition and Results
• Randomize under uncertain adversarial domains
• Research results:– Part 1: Plan randomization with quality constraints
• Information minimization, “no adversary model”• Decision theory
– Part 2: Strategy randomization with quality constraints• Probability distribution over “partial adversary
models”• Game theory
– Part 3: Application to Airport Security
Part I: No Adversary Model Example
• Intentional policy randomization for security– Information Minimization Game– MDP/POMDP: Planning or sequential decision
making under uncertainty ([Note: (PO)MDP is a (Partially Observable) Markov Decision Process]
– Difficult for adversary to predict even if knows policy
• Maintain policy quality constraints– Reward > Threshold– Example: Fuel
Part I: No Adversary Model: Solution Technique
• Single Agent: – Randomized policy with reward > threshold– Nonlinear program: Hard to solve (Exponential)– Convert to Linear Program “BRLP” : Efficient
(Polynomial time)
• Multi Agent Teams: – Randomized policies with reward > threshold
Developed New Algorithms for Plan Randomization
Multi Agent Teams
• Randomize policy for agent teams subject to reward threshold
• Agent teams:– Decentralized POMDPs– No communication possible between agents
• Adversary :– Knows policy of individual team members– Exploits any action predictability
Example Computational Results for Single AgentConclusion: Randomization Recommendation is Computationally Solvable
0123456789
10
50 60 70 80 90 100
Reward Threshold(%)
Ave.
Wei
ghte
d En
tropy BRLP
Hw(x)
Ha(x)
Max Entropy0
20
40
60
80
100
120
50 60 70 80 90 100
Reward Threshold(%)
Exec
utio
n Ti
me
(sec
)
BRLP
Hw(x)
Ha(x)
Part II: Security with Partial Adversary Models
Partial model of adversaries: Example, different adversary preferences
• Crowded terminals• Terminals with military personnel
• How would you allocate canine units?
Part II: Algorithms and Experiments
• “Bayesian Stackelberg Games”– DOBSS: Decomposed Optimal Bayesian Stackelberg Solver– ASAP: Agent Security by Approximate Policies
• Experiments: – Patrolling Domain: Security agent and robber– Security agent patrols houses– Robbers can attack any house– Uncertainty over robber types
Once again, computational solution feasible
• Problem: Setting checkpoints and allocating K9 units?
• Approach: Maximize security through mathematical randomization
• Goal: Create software assistants
PART III: Applications
ARMOR
• Assistant for Randomized Monitoring Over Routes
• DOBSS basis of ARMOR
• ARMOR-Checkpoints
• ARMOR-K9
Assistant for Randomized Monitoring Over Assistant for Randomized Monitoring Over Routes (ARMOR) ProjectRoutes (ARMOR) Project
An Interdisciplinary Counter-Terrorism Research Partnership:
Los Angeles World Airports & The University of Southern California
Deek
Ressam
Jamiiyyat Ul Islam Is Saheeh“The Assembly of Authentic Islam”
Levar Haney Washington
Islam convert
Hammad Riaz SamanaPakistani
Gregory Vernon Patterson - LAX
Mortar Attack
Sniper Attack
Control Tower Bomb
MANPADs Attack
Air Operations Attack
Public Grounds Attack
Curbside Car Bomb
Luggage Bomb
Large Truck Bomb
Uninspected Cargo Bomb
Insider Planted Bomb
Potential Fatalities
Major Threats
Lesser Threats
Terrorist Scenarios Examined (RAND) 12/05
ARMOR SystemARMOR System
DOBSS: GAMETHEORY
ALGORITHMS
Define constraints
RandomizedSchedule
generation
Weights forrandomization
Schedule evaluation
ARMOR Knowledge Base
Knowledge in ARMOR-checkpoint
• ARMOR-checkpoint base requires knowledge:– Numbers of possible checkpoints– Time of checkpoint operation– Traffic flow and its impact on catching adversary– Estimated target priority for adversary– Estimates of cost of getting caught to adversaries– Estimates if “different types” of adversaries and their
probabilities (e.g. differ in their capabilities)
• Converted into utilities
ARMOR Features
• Randomized schedules• Mathematical measure of randomness• Input constraints• Report generation
• Many other parameters to control…
The Element of SurpriseTo help combat the terrorism threat, officials at Los Angeles International Airport are introducing a bold new idea into their arsenal: random placement of security
checkpoints. Can game theory help keep us safe?
Security forces work the sidewalk at LAX
September 28, 2007
Conclusion• New algorithms: guarantee randomness while meeting
quality requirements• Computational techniques that allow practical applications• Initial demonstration with LAX working well, other clients
have expressed interest