Special Issue "Decision Making for Network Security and Privacy"

A special issue of Games (ISSN 2073-4336).

Deadline for manuscript submissions: closed (30 April 2017).

Special Issue Editor

Guest Editor
Dr. Christos Dimitrakakis Website E-Mail
1 INRIA-Lille, University of Lille, Lille, France
2 Department of Computer Science and Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
Interests: decision theory; reinforcement learning; statistical inference and optimisation; privacy and security

Special Issue Information

Dear Colleagues,

Network security and privacy is an important application area, which has inspired a great deal of research in decision theory, statistics, and machine learning. Recent algorithmic and theoretical advances mean that is now possible to employ principled approaches for solving complex decision and estimation problems, such as those encountered in networked systems. Algorithms in the physical, network, and application layer, must take into account the possibility of malicious or honest-but-curious participants, as well as adapt to changing network conditions and system failures.

This Special Issue invites principled research papers on topics at the intersection of network security and privacy on the one hand, and decision theory, machine learning, and statistics on the other. We invite inter-disciplinary papers within this area. Sample topics include: secure multi-party computation, learning in games, network monitoring, intrusion detection and response, privacy-preserving learning and decision-making, differential privacy, and cryptography. However, all submissions must have a clear relevance, or application potential, to network security and privacy.

Dr. Christos Dimitrakakis
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 papers will be 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 quarterly 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 1000 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

  • network security and privacy
  • decision theory
  • secure multi-party computation
  • network monitoring
  • privacy-preserving learning and decision making
  • differential privacy

Published Papers (3 papers)

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Research

Open AccessArticle
Security Investment, Hacking, and Information Sharing between Firms and between Hackers
Games 2017, 8(2), 23; https://doi.org/10.3390/g8020023 - 25 May 2017
Cited by 6
Abstract
A four period game between two firms and two hackers is analyzed. The firms first defend and the hackers thereafter attack and share information. Each hacker seeks financial gain, beneficial information exchange, and reputation gain. The two hackers’ attacks and the firms’ defenses [...] Read more.
A four period game between two firms and two hackers is analyzed. The firms first defend and the hackers thereafter attack and share information. Each hacker seeks financial gain, beneficial information exchange, and reputation gain. The two hackers’ attacks and the firms’ defenses are inverse U-shaped in each other. A hacker shifts from attack to information sharing when attack is costly or the firm’s defense is cheap. The two hackers share information, but a second more disadvantaged hacker receives less information, and mixed motives may exist between information sharing and own reputation gain. The second hacker’s attack is deterred by the first hacker’s reputation gain. Increasing information sharing effectiveness causes firms to substitute from defense to information sharing, which also increases in the firms’ unit defense cost, decreases in each firm’s unit cost of own information leakage, and increases in the unit benefit of joint leakage. Increasing interdependence between firms causes more information sharing between hackers caused by larger aggregate attacks, which firms should be conscious about. We consider three corner solutions. First and second, the firms deter disadvantaged hackers. When the second hacker is deterred, the first hacker does not share information. Third, the first hacker shares a maximum amount of information when certain conditions are met. Policy and managerial implications are provided for how firms should defend against hackers with various characteristics. Full article
(This article belongs to the Special Issue Decision Making for Network Security and Privacy)
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Open AccessArticle
On Information Aggregation and Interim Efficiency in Networks
Games 2017, 8(1), 15; https://doi.org/10.3390/g8010015 - 24 Feb 2017
Abstract
This paper considers a population of agents that are engaged in a listening network. The agents wish to match their actions to the true value of some uncertain (exogenous) parameter and to the actions of the other agents. Each agent begins with some [...] Read more.
This paper considers a population of agents that are engaged in a listening network. The agents wish to match their actions to the true value of some uncertain (exogenous) parameter and to the actions of the other agents. Each agent begins with some initial information about the parameter and, in addition, is able to receive further information from their neighbors in the network. I derive a closed expression for the (interim) social welfare loss that depends on the initial information structure and on the possible pieces of information that can be gathered under the network. Then, I explore how changes in the network may affect social welfare for extreme levels of complementarity in the agents’ actions. When the level of complementarity is very high, efficiency is achieved regardless of the network structure. For very low levels of complementarity in actions, efficiency can be either associated to more sparse or denser networks, depending on the size of the induced informative gains. The implications of this paper are relevant in security environments where agents are naturally interpreted as analysts who try to forecast the value of a parameter that describes a threat to security. Full article
(This article belongs to the Special Issue Decision Making for Network Security and Privacy)
Open AccessArticle
Interdependent Defense Games with Applications to Internet Security at the Level of Autonomous Systems
Games 2017, 8(1), 13; https://doi.org/10.3390/g8010013 - 16 Feb 2017
Abstract
We propose interdependent defense (IDD) games, a computational game-theoretic framework to study aspects of the interdependence of risk and security in multi-agent systems under deliberate external attacks. Our model builds upon interdependent security (IDS) games, a model [...] Read more.
We propose interdependent defense (IDD) games, a computational game-theoretic framework to study aspects of the interdependence of risk and security in multi-agent systems under deliberate external attacks. Our model builds upon interdependent security (IDS) games, a model by Heal and Kunreuther that considers the source of the risk to be the result of a fixed randomized-strategy. We adapt IDS games to model the attacker’s deliberate behavior. We define the attacker’s pure-strategy space and utility function and derive appropriate cost functions for the defenders. We provide a complete characterization of mixed-strategy Nash equilibria (MSNE), and design a simple polynomial-time algorithm for computing all of them for an important subclass of IDD games. We also show that an efficient algorithm to determine whether some attacker’s strategy can be a part of an MSNE in an instance of IDD games is unlikely to exist. Yet, we provide a dynamic programming (DP) algorithm to compute an approximate MSNE when the graph/network structure of the game is a directed tree with a single source. We also show that the DP algorithm is a fully polynomial-time approximation scheme. In addition, we propose a generator of random instances of IDD games based on the real-world Internet-derived graph at the level of autonomous systems (≈27 K nodes and ≈100 K edges as measured in March 2010 by the DIMES project). We call such games Internet games. We introduce and empirically evaluate two heuristics from the literature on learning-in-games, best-response gradient dynamics (BRGD) and smooth best-response dynamics (SBRD), to compute an approximate MSNE in IDD games with arbitrary graph structures, such as randomly-generated instances of Internet games. In general, preliminary experiments applying our proposed heuristics are promising. Our experiments show that, while BRGD is a useful technique for the case of Internet games up to certain approximation level, SBRD is more efficient and provides better approximations than BRGD. Finally, we discuss several extensions, future work, and open problems. Full article
(This article belongs to the Special Issue Decision Making for Network Security and Privacy)
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