28,633 research outputs found
Game Based Learning for Safety and Security Education
Safety and security education are important part of technology related education, because of recent number of increase in safety and security related incidents. Game based learning is an emerging and rapidly advancing forms of computer-assisted instruction. Game based learning for safety and security education enables students to learn concepts and skills without the risk of physical injury and security breach. In this paper, a pedestal grinder safety game and physical security game have been developed using industrial standard modeling and game development software. The average score of the knowledge test of grinder safety game was 82%, which is higher than traditional lecture only instruction method. In addition, the survey of physical security game shows 84% average satisfaction ratio from high school students who played the game during the summer camp. The results of these studies indicated that game based learning method can enhance students' learning without potential harm to the students
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Symbolic Abstractions for Quantum Protocol Verification
Quantum protocols such as the BB84 Quantum Key Distribution protocol exchange
qubits to achieve information-theoretic security guarantees. Many variants
thereof were proposed, some of them being already deployed. Existing security
proofs in that field are mostly tedious, error-prone pen-and-paper proofs of
the core protocol only that rarely account for other crucial components such as
authentication. This calls for formal and automated verification techniques
that exhaustively explore all possible intruder behaviors and that scale well.
The symbolic approach offers rigorous, mathematical frameworks and automated
tools to analyze security protocols. Based on well-designed abstractions, it
has allowed for large-scale formal analyses of real-life protocols such as TLS
1.3 and mobile telephony protocols. Hence a natural question is: Can we use
this successful line of work to analyze quantum protocols? This paper proposes
a first positive answer and motivates further research on this unexplored path
Information Leakage Games
We consider a game-theoretic setting to model the interplay between attacker
and defender in the context of information flow, and to reason about their
optimal strategies. In contrast with standard game theory, in our games the
utility of a mixed strategy is a convex function of the distribution on the
defender's pure actions, rather than the expected value of their utilities.
Nevertheless, the important properties of game theory, notably the existence of
a Nash equilibrium, still hold for our (zero-sum) leakage games, and we provide
algorithms to compute the corresponding optimal strategies. As typical in
(simultaneous) game theory, the optimal strategy is usually mixed, i.e.,
probabilistic, for both the attacker and the defender. From the point of view
of information flow, this was to be expected in the case of the defender, since
it is well known that randomization at the level of the system design may help
to reduce information leaks. Regarding the attacker, however, this seems the
first work (w.r.t. the literature in information flow) proving formally that in
certain cases the optimal attack strategy is necessarily probabilistic
Analysis domain model for shared virtual environments
The field of shared virtual environments, which also
encompasses online games and social 3D environments, has a
system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model
Ergodic mean-payo games for the analysis of attacks in crypto-currencies
Crypto-currencies are digital assets designed to work as a medium of exchange, e.g., Bitcoin, but they are susceptible to attacks (dishonest behavior of participants). A framework for the analysis of attacks in crypto-currencies requires (a) modeling of game-theoretic aspects to analyze incentives for deviation from honest behavior; (b) concurrent interactions between participants; and (c) analysis of long-term monetary gains. Traditional game-theoretic approaches for the analysis of security protocols consider either qualitative temporal properties such as safety and termination, or the very special class of one-shot (stateless) games. However, to analyze general attacks on protocols for crypto-currencies, both stateful analysis and quantitative objectives are necessary. In this work our main contributions are as follows: (a) we show how a class of concurrent mean-payo games, namely ergodic games, can model various attacks that arise naturally in crypto-currencies; (b) we present the first practical implementation of algorithms for ergodic games that scales to model realistic problems for crypto-currencies; and (c) we present experimental results showing that our framework can handle games with thousands of states and millions of transitions
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