Security via Strategic Randomization Milind Tambe Fernando Ordonez Praveen Paruchuri Sarit Kraus (Bar Ilan, Israel) Jonathan Pearce, Jansuz Marecki James Pita, Christopher Portway University of Southern California Los Angeles November, 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 Agent Conclusion: Randomization Recommendation is Computationally Solvable

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 Routes (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 Samana Pakistani 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 System DOBSS: GAME THEORY ALGORITHMS Define constraints Randomized Schedule generation Weights for randomization 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 Surprise To 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