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Games
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Game-Theoretic Analysis of Network Security and Privacy
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Game-Theoretic Analysis of Network Security 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 (30 July 2023) | Viewed by 6386

Special Issue Editor

Dr. Yevgeniy Vorobeychik

E-Mail Website
Guest Editor
Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: adversarial machine learning; computational game theory; security games; privacy and game theory; network science

Special Issue Information

Dear Colleagues,

Security and privacy settings can be naturally modeled as strategic interactions among defenders, who are concerned with protecting their systems, and attackers, who aspire to compromise the security or privacy of these systems.  This Special Issue is devoted to the study of game-theoretic models of security and privacy on networks, where networks are viewed as general abstractions that can represent interaction and interdependence among devices and/or people. Of particular interest to this Special Issue are novel game-theoretic models in CPS/IoT security and privacy, game-theoretic reasoning in network analytics, game-theoretic approaches that aim to balance privacy and data utility, and approaches at the intersection of game theory and machine learning in security and privacy.

Dr. Yevgeniy Vorobeychik
Guest Editor

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

  • game theory and security
  • game theory and privacy
  • adversarial network analysis
  • adversarial machine learning
  • game theory in CPS

Published Papers (4 papers)

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Research

23 pages, 486 KiB  
Article
Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments
by Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark and Radha Poovendran
Games 2023, 14(2), 30; https://doi.org/10.3390/g14020030 - 30 Mar 2023
Viewed by 1187
Abstract
This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex time-sensitive tasks in the presence of an adversary. The time-sensitive task is specified as a formula in the metric interval temporal logic (MITL). CPSs that [...] Read more.
This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex time-sensitive tasks in the presence of an adversary. The time-sensitive task is specified as a formula in the metric interval temporal logic (MITL). CPSs that operate in adversarial environments have typically been abstracted as stochastic games (SGs); however, because traditional SG models do not incorporate a notion of time, they cannot be used in a setting where the objective is time-sensitive. To address this, we introduce durational stochastic games (DSGs). DSGs generalize SGs to incorporate a notion of time and model the adversary’s abilities to tamper with the control input (actuator attack) and manipulate the timing information that is perceived by the CPS (timing attack). We define notions of spatial, temporal, and spatio-temporal robustness to quantify the amounts by which system trajectories under the synthesized policy can be perturbed in space and time without affecting satisfaction of the MITL objective. In the case of an actuator attack, we design computational procedures to synthesize controllers that will satisfy the MITL task along with a guarantee of its robustness. In the presence of a timing attack, we relax the robustness constraint to develop a value iteration-based procedure to compute the CPS policy as a finite-state controller to maximize the probability of satisfying the MITL task. A numerical evaluation of our approach is presented on a signalized traffic network to illustrate our results. Full article
(This article belongs to the Special Issue Game-Theoretic Analysis of Network Security and Privacy)
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19 pages, 912 KiB  
Article
Deterrence, Backup, or Insurance: Game-Theoretic Modeling of Ransomware
by Tongxin Yin, Armin Sarabi and Mingyan Liu
Games 2023, 14(2), 20; https://doi.org/10.3390/g14020020 - 23 Feb 2023
Cited by 1 | Viewed by 1574
Abstract
In this paper, we present a game-theoretic analysis of ransomware. To this end, we provide theoretical and empirical analysis of a two-player Attacker-Defender (A-D) game, as well as a Defender-Insurer (D-I) game; in the latter, the attacker is assumed to be a non-strategic [...] Read more.
In this paper, we present a game-theoretic analysis of ransomware. To this end, we provide theoretical and empirical analysis of a two-player Attacker-Defender (A-D) game, as well as a Defender-Insurer (D-I) game; in the latter, the attacker is assumed to be a non-strategic third party. Our model assumes that the defender can invest in two types of protection against ransomware attacks: (1) general protection through a deterrence effort, making attacks less likely to succeed, and (2) a backup effort serving the purpose of recourse, allowing the defender to recover from successful attacks. The attacker then decides on a ransom amount in the event of a successful attack, with the defender choosing to pay ransom immediately, or to try to recover their data first while bearing a recovery cost for this recovery attempt. Note that recovery is not guaranteed to be successful, which may eventually lead to the defender paying the demanded ransom. Our analysis of the A-D game shows that the equilibrium falls into one of three scenarios: (1) the defender will pay the ransom immediately without having invested any effort in backup, (2) the defender will pay the ransom while leveraging backups as a credible threat to force a lower ransom demand, and (3) the defender will try to recover data, only paying the ransom when recovery fails. We observe that the backup effort will be entirely abandoned when recovery is too expensive, leading to the (worst-case) first scenario which rules out recovery. Furthermore, our analysis of the D-I game suggests that the introduction of insurance leads to moral hazard as expected, with the defender reducing their efforts; less obvious is the interesting observation that this reduction is mostly in their backup effort. Full article
(This article belongs to the Special Issue Game-Theoretic Analysis of Network Security and Privacy)
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18 pages, 883 KiB  
Article
Information Design for Multiple Interdependent Defenders: Work Less, Pay Off More
by Chenghan Zhou, Andrew Spivey, Haifeng Xu and Thanh H. Nguyen
Games 2023, 14(1), 12; https://doi.org/10.3390/g14010012 - 30 Jan 2023
Viewed by 1475
Abstract
This paper studies the problem of information design in a general security game setting in which multiple self-interested defenders attempt to provide protection simultaneously for the same set of important targets against an unknown attacker. A principal, who can be one of the [...] Read more.
This paper studies the problem of information design in a general security game setting in which multiple self-interested defenders attempt to provide protection simultaneously for the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type), whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. In particular, we develop a polynomial time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness for the targets. Full article
(This article belongs to the Special Issue Game-Theoretic Analysis of Network Security and Privacy)
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24 pages, 3023 KiB  
Article
A Complete Analysis on the Risk of Using Quantal Response: When Attacker Maliciously Changes Behavior under Uncertainty
by Thanh Hong Nguyen and Amulya Yadav
Games 2022, 13(6), 81; https://doi.org/10.3390/g13060081 - 02 Dec 2022
Viewed by 1260
Abstract
In security games, the defender often has to predict the attacker’s behavior based on some observed attack data. However, a clever attacker can intentionally change its behavior to mislead the defender’s learning, leading to an ineffective defense strategy. This paper investigates the attacker’s [...] Read more.
In security games, the defender often has to predict the attacker’s behavior based on some observed attack data. However, a clever attacker can intentionally change its behavior to mislead the defender’s learning, leading to an ineffective defense strategy. This paper investigates the attacker’s imitative behavior deception under uncertainty, in which the attacker mimics a (deceptive) Quantal Response behavior model by consistently playing according to a certain parameter value of that model, given that it is uncertain about the defender’s actual learning outcome. We have three main contributions. First, we introduce a new maximin-based algorithm to compute a robust attacker deception decision under uncertainty, given the defender is unaware of the attacker deception. Our polynomial algorithm is built via characterizing the decomposability of the attacker deception space as well optimal deception behavior of the attacker against the worst case of uncertainty. Second, we propose a new counter-deception algorithm to tackle the attacker’s deception. We theoretically show that there is a universal optimal defense solution, regardless of any private knowledge the defender has about the relation between their learning outcome and the attacker deception choice. Third, we conduct extensive experiments in various security game settings, demonstrating the effectiveness of our proposed counter-deception algorithms to handle the attacker manipulation. Full article
(This article belongs to the Special Issue Game-Theoretic Analysis of Network Security and Privacy)
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