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Board games have often been recognised as a tool to model complex concepts in abstract environments for entertainment, education, and research in fields such as military and artificial intelligence. With more board games being designed and published, it is timely to draw attention towards board game design strategies and mechanics which capture the attributes that drive game play. The game design and the mechanics used define the structure, functionality, and play experience of these games. Towards this end, this paper presents a data driven review of board game mechanics and play-related attributes, their interactions and relationships. The analysis expects to draw insights into how board games can be utilised across diverse domains as a tool to understand and explore complex concepts through abstract models. The investigations focus on identifying the trends and patterns of board games being published and their individual mechanics over time. Moreover, the correlation between mechanics and play-related attributes such as game complexity, rating, and duration are explored. The interactions and similarities between individual mechanics based on co-occurrence, mutual information, and clustering based approaches are also illustrated. The results show that the level of complexity and engagement of a game is not a simple function of the set of mechanics used, but rather the interactions that exist between mechanics, and the nature of their specific implementation are the critical factors in determining play experience of a board game.

This paper outlines a novel approach to 3D visualization of network traffic. Existing approaches, which present node-graphs in 3D space may not be making the best use of the advantages of 3D. By combining the time component of network traffic data with nodal information and displaying these on separate planes it should be possible to provide analysts with insights that go beyond just the nodal information. The goal of allowing analysts to quickly form a mental map that corresponds with the network traffic ground truth may be achieved with this approach. The visualization approach is demonstrated through development of a tool which implements the approach and discusses its application to a recent network forensics challenge.

Modern power systems depend on cyber-physical systems to link physical devices and control technologies. A major concern in the implementation of smart power networks is to minimize the risk of data privacy violation (e.g., by adversaries using data poisoning and inference attacks). In this article, we propose a privacy-preserving framework to achieve both privacy and security in smart power networks. The framework includes two main modules: A two-level privacy module and an anomaly detection module. In the two-level privacy module, an enhanced-proof-of-work-Technique-based blockchain is designed to verify data integrity and mitigate data poisoning attacks, and a variational autoencoder is simultaneously applied for transforming data into an encoded format for preventing inference attacks. In the anomaly detection module, a long short-Term memory deep learning technique is used for training and validating the outputs of the two-level privacy module using two public datasets. The results highlight that the proposed framework can efficiently protect data of smart power networks and discover abnormal behaviors, in comparison to several state-of-The-Art techniques.

This paper presents a new method for generation of simulated fractures where fractures ‘grow’ dynamically, permitting interaction during formation. These interactions mimic the natural processes of stress shadowing, termination of fractures on other fractures, linking of fractures, and the varied growth on a single fracture due to contrasting rock properties. By simulating these interactions and providing the user with fine control over them, the new fracture generation method can create simulated fracture networks that match natural fracture networks more closely than other established methods. These behaviours are implemented mechanistically, allowing fracture generation to be achieved without the significant additional computational cost required to explicitly model the stresses in a fracturing rock volume. A suite of test cases is demonstrated, illustrating how different configurations of the dynamic fracture model allows different connectivity characteristics to be modelled., QC 20200618

The cloud computing paradigm is changing how businesses operate, providing greater effi- ciency, tolerance, elasticity and fiexibility in computing workloads. Underpinning these changes are multiple data centers, operated by different entities and distributed globally. Despite these benefits, cloud computing presents new classes of cyber-attack, opportunities to attack and processes to subvert. One of the primary strategies to defend against cyber-attacks is the migration process. A secure Virtual Machine (VM) migration is essential to safeguard cloud data centers against insider and outsider attacks. In this paper, we propose a collaborative anomaly detection system for discovering insider and outsider attacks from cloud systems and their migration process. The proposed system is named Mixture Localization-based Outliers (MLO) and utilizes Gaussian-mixture models for fitting network data and a local outlier factor function for discovering abnormal patterns in network traffic data. In order to validate the effectiveness of the models, the datasets of UNSW-NB15 and BoT-IoT are employed. The experimental results have revealed the high performance of the proposed system compared with several peer anomaly detection techniques.

Despite the academic and military interest in the use of combined cyber-kinetic effects in future warfare, there is little that seeks to scientifically analyse the impact of combined cyber and kinetic military action. Current approaches to simulation focus on either the physical or cyber, but there are no current simulation approaches that combine these two domains. Military simulations in particular favor physical domains over cyber ones, relegating the uniqueness and nuances of cyber a second order consideration. Future warfare will incorporate a combined multi-domain conflict, and this includes cyber. To effectively simulate this, the uniqueness of each domain must be considered, including the interaction between domains to produce combined effects. This work introduces the Battlespace Integrated Cyber-Kinetic Effects (BICKE) simulation framework. The BICKE framework is designed specifically to combine the cyber and physical domains for the purposes of simulating mission impact. There is a requirement for researchers to measure the synergistic effects across domains.