New Zealand

Alessandra Budillon (October 1st 2009). Moving Target Detection and Velocity Estimation in Multi-Channel AT-InSAR Systems from Amplitude and Phase Data, Geoscience and Remote Sensing, Pei-Gee Peter Ho, IntechOpen, DOI: 10.5772/8304. 

Moving target defense has emerged as a critical paradigm of protecting a vulnerable system against persistent and stealthy attacks. To protect a system, a defender proactively changes the system configurations to limit the exposure of security vulnerabilities to potential attackers. In doing so, the defender creates asymmetric uncertainty and complexity for the attackers, making it much harder for them to compromise the system. In practice, the defender incurs a switching cost for each migration of the system configurations. The switching cost usually depends on both the current configuration and the following configuration. Besides, different system configurations typically require a different amount of time for an attacker to exploit and attack. Therefore, a defender must simultaneously decide both the optimal sequences of system configurations and the optimal timing for switching. In this paper, we propose a Markov Stackelberg Game framework to precisely characterize the defender&#39;s spatial and temporal decision-making in the face of advanced attackers. We introduce a relative value iteration algorithm that computes the defender&#39;s optimal moving target defense strategies. Empirical evaluation on real-world problems demonstrates the advantages of the Markov Stackelberg game model for spatial-temporal moving target defense.

AAMAS 2020

Spatial-Temporal Moving Target Defense: A Markov Stackelberg Game Model

moving target defense

stackelberg game

markov decision process

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

Moving target defense has emerged as a critical paradigm of protecting a vulnerable system against persistent and stealthy attacks. To protect a system, a defender proactively changes the system configurations to limit the exposure of security vulnerabilities to potential attackers. In doing so, the defender creates asymmetric uncertainty and complexity for the attackers, making it much harder for them to compromise the system. In practice, the defender incurs a switching cost for each migration of the system configurations. The switching cost usually depends on both the current configuration and the following configuration. Besides, different system configurations typically require a different amount of time for an attacker to exploit and attack. Therefore, a defender must simultaneously decide both the optimal sequences of system configurations and the optimal timing for switching. In this paper, we propose a Markov Stackelberg Game framework to precisely characterize the defender's spatial and temporal decision-making in the face of advanced attackers. We introduce a relative value iteration algorithm that computes the defender's optimal moving target defense strategies. Empirical evaluation on real-world problems demonstrates the advantages of the Markov Stackelberg game model for spatial-temporal moving target defense.

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Introducing DCRAC, a deep reinforcement learning framework for solving multi-objective partially-observable problems. DCRAC follows a conditioned actor-critic approach in
learning the optimal policy.

DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments

This tutorial will survey results on fairness in resource allocation. Starting from the classical setting of cake-cutting, the tutorial will explore the allocation of divisible and indivisible goods, public decision-making, and online resource allocation. The tutorial will include various formal definitions of fairness, relations among these definitions, and algorithms for (and complexity of) satisfying such definitions.

Recent Advances in Fair Resource Allocation Tutorial Part 1

A quadcopter can capture photos from vantage points unattainable for a human photographer, but teloperating it to a good viewpoint is a non-trivial task. Since humans are good at composing photos, the aim of our research is to leverage deep learning to create a customizable flight controller that can capture photos under the guidance of a human photographer. Our system, the Selfie Drone Stick, allows the user to assign a vantage point to the quadcopter based on the phone's sensors. The user takes a selfie with the phone once, and the quadcopter autonomously flies to the target viewpoint. The proliferation of open source deep learning models provided us with a large variety of options for the computer vision and flight control systems. This article describes three key innovations required to deploy the models on a real robot: 1) a new architecture for rapid object detection, DUNet; 2) an abstract state representation for transferring learning from simulation to the hardware platform; 3) reward shaping and staging paradigms for training a deep reinforcement learning controller. Without these improvements, we were unable to learn a flight controller that adequately supported the intuitive user interface.

Leveraging Deep Learning Models to Create a Natural Interface for Quadcopter Photography

Ethics in Sociotechnical Systems

Live Simulations

Agents are Dead Long Live Agents !

A Multi-Robot Platform for the Autonomous Operation and Maintenance of Offshore Wind Farms

The DALI system aims at making existing conventional traffic control systems autonomous and smart. We achieve this goal by plugging-in a software agent into each existing intersection controller which becomes “the brain” of the controller. The agents analyze the traffic data, communicate with each other directly, and collaborate to execute a timing strategy that improves traffic flow.

DALI: An Agent-Plug-In System to “Smartify” Conventional Traffic Control Systems

Deep Reinforcement Learning for Market Making

This video presentation presents the main work we have done for investigating of Multi-agent Reinforcement Learning for Multi-traffic Light Control.

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DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments

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When Can the Defender Effectively Deceive Attackers in Security Games?

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Next from AAMAS 2020

DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments

Similar lecture

When Can the Defender Effectively Deceive Attackers in Security Games?

Stay up to date with the latest Underline news!

PRESENTATIONS

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COMPANY

RESOURCES

Downloads