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Trust models play an important role in computational environments. One of the main aims of the work undertaken in this domain is to provide a model that can better describe the socio-technical nature of computational trust. It has been recently shown that quantum-like formulations in the field of human decision making can better explain the underlying nature of these types of processes. Based on this research, the aim of this paper is to propose a novel model of trust based on quantum probabilities as the underlying mathematics of quantum theory. It will be shown that by using this new mathematical framework, we will have a powerful mechanism to model the contextuality property of trust. Also, it is hypothesized that many events or evaluations in the context of trust can be and should be considered as incompatible, which is unique to the noncommutative structure of quantum probabilities. The main contribution of this paper will be that, by using the quantum Bayesian inference mechanism for belief updating in the framework of quantum theory, we propose a biased trust inference mechanism. This mechanism allows us to model the negative and positive biases that a trustor may subjectively feel toward a certain trustee candidate. It is shown that by using this bias, we can model and describe the exploration versus exploitation problem in the context of trust decision making, recency effects for recently good or bad transactions, filtering pessimistic and optimistic recommendations that may result in good-mouthing or badmouthing attacks, the attitude of the trustor toward risk and uncertainty in different situations and the pseudo-transitivity property of trust. Finally, we have conducted several experimental evaluations in order to demonstrate the effectiveness of the proposed model in different scenarios.

The aim of this work is to propose a framework for the distributed simulation of cyber attacks based on high-level architecture (HLA), which is a commonly used standard for distributed simulations. The proposed framework and the corresponding simulator, which is called the distributed cyber attack simulator (abbreviated by DCAS), help administrators to model and evaluate the security measures of the networks. At the core of the DCAS is a simulation engine based on Portico, which is an open source HLA run-time infrastructure. The DCAS works in two modes: interactive and automated. Three types of simulation components (which are called federates in HLA terminology) are considered in the framework: the (1) network federate, (2) attacker federate and (3) defender federate. The simulator provides features for graphical design of the network models, animated traffic simulation, data collection, statistical analysis and different consoles for attacking and defending elements (e.g., intrusion detection systems, intrusion prevention systems). To increase the fidelity of the simulation outputs, real-world payloads are used by the DCAS. All the exploits information and the parameters of various network elements are automatically extracted from the open source vulnerability database. Also, the Snort rule-set is used as the signature database of the defending elements. The architecture and algorithms of the DCAS and the corresponding underlying simulation engine plus the security evaluation results of two illustrative examples are presented in this paper.

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Abstract In the current state of the video game productions, most of the video game levels are created by the human operators working as level designers. This manual process is not only time-consuming and resource-intensive but also hard to guarantee uniform quality in the contents created by the level designers. One way to address this issue is to use computer-assisted level design techniques. In this paper, we have proposed a novel framework for computer-assisted video game level design that leverages neural networks, particularly generative adversarial networks (GANs) and autoencoders. The general idea is to learn over a dataset of high-quality levels and subsequently improve the ones created by the level designers. The proposed method is independent of the graphical dimensionality of the game and will work for 2D and 3D games in general. The autoencoder is used to create an intermediate representation of the level that is itself changed using the backpropagation technique according to the feedback obtained by feeding the output of the autoencoder to the discriminator component of the GAN. After performing a series of evaluations on the proposed framework and by automatically improving a series of purposefully corrupted game levels, the results demonstrate a noticeable improvement compared with the usage of simple autoencoders used to improve the video game levels in the previous researches.