Special Issue "Game Theory for Cybersecurity and Privacy"

A special issue of Games (ISSN 2073-4336). This special issue belongs to the section "Algorithmic and Computational Game Theory".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2877

Special Issue Editors

Department of Computer Science, University of Delaware, Newark, DE 19716, USA
Interests: data privacy; algorithmic economics; game theory and mechanism design; security economics; algorithmic fairness
Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
Interests: machine learning; fairness and privacy; sequential decision making; distributed algorithms; economics of security

Special Issue Information

Dear Colleagues,

Cyber technologies have brought enormous benefits to society and made people and communities more connected. However, these technologies have also provided opportunities for cyber attacks. These attacks can compromise personal and sensitive data, cause business interruptions and ruin companies' assets. To address these security issues, we need to have mechanisms in place to incentivize organizations to fix security issues and adopt a proper defense strategy against future attacks. This Special Issue of Games is devoted to studying and analyzing cybersecurity and privacy from the perspective of game theory. We welcome authors to submit their research on topics including, but not limited to: optimal investment in information security, incentive design for information sharing, models and analysis of cybercrime, cyber-security policy, the economics of privacy and anonymity, cyber-defense strategy, cyber insurance market, cryptocurrency markets, and cybersecurity vulnerability market.

Dr. Mohammad Mahdi Khalili
Dr. Xueru Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Games is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • security game
  • economics of security and privacy
  • game theory
  • mechanism design
  • data market
  • information security

Published Papers (2 papers)

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Research

Article
Defining Cyber Risk Scenarios to Evaluate IoT Systems
Games 2023, 14(1), 1; https://doi.org/10.3390/g14010001 - 20 Dec 2022
Cited by 1 | Viewed by 1222
Abstract
The growth of the Internet of Things (IoT) has accelerated digital transformation processes in organizations and cities. However, it has also opened new security challenges due to the complexity and dynamism of these systems. The application of security risk analysis methodologies used to [...] Read more.
The growth of the Internet of Things (IoT) has accelerated digital transformation processes in organizations and cities. However, it has also opened new security challenges due to the complexity and dynamism of these systems. The application of security risk analysis methodologies used to evaluate information technology (IT) systems have their limitations to qualitatively assess the security risks in IoT systems, due to the lack of historical data and the dynamic behavior of the solutions based on the IoT. The objective of this study is to propose a methodology for developing a security risk analysis using scenarios based on the risk factors of IoT devices. In order to manage the uncertainty due to the dynamics of IoT behaviors, we propose the use of Bayesian networks in conjunction with the Best Worst Method (BWM) for multi-criteria decision-making to obtain a quantitative security risk value. Full article
(This article belongs to the Special Issue Game Theory for Cybersecurity and Privacy)
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Article
RewardRating: A Mechanism Design Approach to Improve Rating Systems
Games 2022, 13(4), 52; https://doi.org/10.3390/g13040052 - 29 Jul 2022
Viewed by 1192
Abstract
Nowadays, rating systems play a crucial role in the attraction of customers to different services. However, as it is difficult to detect a fake rating, fraudulent users can potentially unfairly impact the rating’s aggregated score. This fraudulent behavior can negatively affect customers and [...] Read more.
Nowadays, rating systems play a crucial role in the attraction of customers to different services. However, as it is difficult to detect a fake rating, fraudulent users can potentially unfairly impact the rating’s aggregated score. This fraudulent behavior can negatively affect customers and businesses. To improve rating systems, in this paper, we take a novel mechanism-design approach to increase the cost of fake ratings while providing incentives for honest ratings. However, designing such a mechanism is a challenging task, as it is not possible to detect fake ratings since raters might rate a same service differently. Our proposed mechanism RewardRating is inspired by the stock market model in which users can invest in their ratings for services and receive a reward on the basis of future ratings. We leverage the fact that, if a service’s rating is affected by a fake rating, then the aggregated rating is biased toward the direction of the fake rating. First, we formally model the problem and discuss budget-balanced and incentive-compatibility specifications. Then, we suggest a profit-sharing scheme to cover the rating system’s requirements. Lastly, we analyze the performance of our proposed mechanism. Full article
(This article belongs to the Special Issue Game Theory for Cybersecurity and Privacy)
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