15,758 research outputs found
Game-theoretic Robustness of Many-to-one Networks
In this paper, we study the robustness of networks that are
characterized by many-to-one communications (e.g., access
networks and sensor networks) in a game-theoretic model. More
specifically, we model the interactions between a network
operator and an adversary as a two player zero-sum game, where
the network operator chooses a spanning tree in the network, the
adversary chooses an edge to be removed from the network, and
the adversary’s payoff is proportional to the number of nodes
that can no longer reach a designated node through the spanning
tree. We show that the payoff in every Nash equilibrium of the
game is equal to the reciprocal of the persistence of the
network. We describe optimal adversarial and operator strategies
and give efficient, polynomial-time algorithms to compute
optimal strategies. We also generalize our game model to include
varying node weights, as well as attacks against nodes
Multi-Layer Cyber-Physical Security and Resilience for Smart Grid
The smart grid is a large-scale complex system that integrates communication
technologies with the physical layer operation of the energy systems. Security
and resilience mechanisms by design are important to provide guarantee
operations for the system. This chapter provides a layered perspective of the
smart grid security and discusses game and decision theory as a tool to model
the interactions among system components and the interaction between attackers
and the system. We discuss game-theoretic applications and challenges in the
design of cross-layer robust and resilient controller, secure network routing
protocol at the data communication and networking layers, and the challenges of
the information security at the management layer of the grid. The chapter will
discuss the future directions of using game-theoretic tools in addressing
multi-layer security issues in the smart grid.Comment: 16 page
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
Present attack methods can make state-of-the-art classification systems based
on deep neural networks misclassify every adversarially modified test example.
The design of general defense strategies against a wide range of such attacks
still remains a challenging problem. In this paper, we draw inspiration from
the fields of cybersecurity and multi-agent systems and propose to leverage the
concept of Moving Target Defense (MTD) in designing a meta-defense for
'boosting' the robustness of an ensemble of deep neural networks (DNNs) for
visual classification tasks against such adversarial attacks. To classify an
input image, a trained network is picked randomly from this set of networks by
formulating the interaction between a Defender (who hosts the classification
networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg
Game (BSG). We empirically show that this approach, MTDeep, reduces
misclassification on perturbed images in various datasets such as MNIST,
FashionMNIST, and ImageNet while maintaining high classification accuracy on
legitimate test images. We then demonstrate that our framework, being the first
meta-defense technique, can be used in conjunction with any existing defense
mechanism to provide more resilience against adversarial attacks that can be
afforded by these defense mechanisms. Lastly, to quantify the increase in
robustness of an ensemble-based classification system when we use MTDeep, we
analyze the properties of a set of DNNs and introduce the concept of
differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security
(GameSec), 201
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
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