Epidemic Protection Over Heterogeneous Networks Using Evolutionary Poisson Games

IEEE Trans. Control Netw. Syst.

2020

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Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency for information exchange. However, malware and virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use an SIS epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus-resistance can be achieved by a distributed weight adaptation scheme.

“…Hayel et.al. in [3], [11] have studied large population game with heterogeneous types of individuals. They focus on group behavior of certain type in stead of individual behavior.…”

Section: Agents' Responsementioning

confidence: 99%

Section: Inefficiency Of Nash Equilibriummentioning

confidence: 99%

See 1 more Smart Citation

A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Huang

IEEE Trans. Control Netw. Syst.

2020

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“…The tutorial will be organized to connect different classes of games with different sets of security problems. For example (1) Stackelberg and multi-layer games for proactive defense [8, 26, 68, 70-72, 76, 80, 82], (2) network games for cyber-physical security that deals with critical infrastructure protection and information assurance [3,15,21,36,54,[56][57][58][59][60], (3) dynamic games for adaptive defense for network security [9,16,17,24,66,67,78,79,81], (4) mechanism design theory for economics of network security that investigates resource allocation methodologies [2,5,11,13,61,64,[73][74][75], and (5) game-theoretic analysis of cryptographic concepts, such as perfectly confidentiality and authentication (in classical and quantum networks) [47][48][49][50], network design and -provisioning [30-32, 45, 46, 53] and quantitative security risk management [33-35, 38-44, 51, 52] From the perspective of cybersecurity, the topics of this tutorial will cover recent applications of game theory to several emerging topics such as cross-layer cyber-physical security [6,21,29,59,69,77], cyber deception…”

Section: Tutorial Descriptionmentioning

confidence: 99%

Game Theory Meets Network Security

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

Raß

2018

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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 costprohibitive. 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. CCS CONCEPTS• Security and privacy → Network security; • Mathematics of computing; • Theory of computation → Algorithmic game theory and mechanism design;

“…Signaling games are often used to model deception and trust in cybersecurity [16,19,21]. Poisson games have also been used to model malware epidemics in large populations [11]. Wu et al use game theory to design defense mechanisms against DDoS attacks [24].…”

mentioning

confidence: 99%

Proactive Defense Against Physical Denial of Service Attacks Using Poisson Signaling Games

Pawlick

Lecture Notes in Computer Science

2017

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While the Internet of things (IoT) promises to improve areas such as energy efficiency, health care, and transportation, it is highly vulnerable to cyberattacks. In particular, distributed denial-of-service (DDoS) attacks overload the bandwidth of a server. But many IoT devices form part of cyber-physical systems (CPS). Therefore, they can be used to launch "physical" denial-of-service attacks (PDoS) in which IoT devices overflow the "physical bandwidth" of a CPS. In this paper, we quantify the population-based risk to a group of IoT devices targeted by malware for a PDoS attack. In order to model the recruitment of bots, we develop a "Poisson signaling game," a signaling game with an unknown number of receivers, which have varying abilities to detect deception. Then we use a version of this game to analyze two mechanisms (legal and economic) to deter botnet recruitment. Equilibrium results indicate that 1) defenders can bound botnet activity, and 2) legislating a minimum level of security has only a limited effect, while incentivizing active defense can decrease botnet activity arbitrarily. This work provides a quantitative foundation for proactive PDoS defense.