Model-Driven Cyber Range Training: A Cyber Security Assurance Perspective

Security demands are increasing for all types of organisations, due to the ever-closer integration of computing infrastructures and smart devices into all aspects of the organisational operations. Consequently, the need for security-aware employees in every role of an organisation increases in accordance. Cyber Range training emerges as a promising solution, allowing employees to train in both realistic environments and scenarios and gaining hands-on experience in security aspects of varied complexity, depending on their role and level of expertise. To that end, this work introduces a model-driven approach for Cyber Range training that facilitates the generation of tailor-made training scenarios based on a comprehensive model-based description of the organisation and its security posture. Additionally, our approach facilitates the auto- mated deployment of such training environments, tailored to each defined scenario, through simulation and emulation means. To further highlight the usability of the proposed approach, this work also presents scenarios focusing on phishing threats, with increasing level of complexity and difficulty

Virtual Reality Interactive Learning Environment

Open Building Manufacturing (ManuBuild) aims to promote the European construction industry beyond the state of the art. However, this requires the different stakeholders to be well informed of what ‘Open Building Manufacturing’ actually entails with respect to understanding the underlying concepts, benefits and risks. This is further challenged by the ‘traditional ways of learning’ which have been predominantly criticised for being entrenched in theories with little or no emphasis on practical issues. Experiential learning has long been suggested to overcome the problems associated with the traditional ways of learning. In this respect, it has the dual benefit of appealing to adult learner's experience base, as well as increasing the likelihood of performance change through training. On-the-job-training (OJT) is usually sought to enable ‘experiential’ learning; and it is argued to be particularly effective in complex tasks, where a great deal of independence is granted to the task performer. However, OJT has been criticised for being expensive, limited, and devoid of the actual training context. Consequently, in order to address the problems encountered with OJT, virtual reality (VR) solutions have been proposed to provide a risk free environment for learning without the ‘do-or-die’ consequences often faced on real construction projects. Since ManuBuild aims to promote the EU construction industry beyond the state of the art; training and education therefore needs also to go beyond the state of the art in order to meet future industry needs and expectations. Hence, a VR interactive learning environment was suggested for Open Building Manufacturing training to allow experiential learning to take place in a risk free environment, and consequently overcome the problems associated with OJT. This chapter discusses the development, testing, and validation of this prototype

Towards a Lightweight Approach for Modding Serious Educational Games: Assisting Novice Designers

Serious educational games (SEGs) are a growing segment of the education community’s pedagogical toolbox. Effectively creating such games remains challenging, as teachers and industry trainers are content experts; typically they are not game designers with the theoretical knowledge and practical experience needed to create a quality SEG. Here, a lightweight approach to interactively explore and modify existing SEGs is introduced, a toll that can be broadly adopted by educators for pedagogically sound SEGs. Novice game designers can rapidly explore the educational and traditional elements of a game, with a stress on tracking the SEG learning objectives, as well as allowing for reviewing and altering a variety of graphic and audio game elements

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Learning robot policies using a high-level abstraction persona-behaviour simulator

2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCollecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients’ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user’s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user’s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user’s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.Peer ReviewedPostprint (author's final draft

Towards a data-driven approach to scenario generation for serious games

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario’s training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance

Designing for designers: Towards the development of accessible ICT products and services using the VERITAS framework

Among key design practices which contribute to the development of inclusive ICT products and services is user testing with people with disabilities. Traditionally, this involves partial or minimal user testing through the usage of standard heuristics, employing external assisting devices, and the direct feedback of impaired users. However, efficiency could be improved if designers could readily analyse the needs of their target audience. The VERITAS framework simulates and systematically analyses how users with various impairments interact with the use of ICT products and services. Findings show that the VERITAS framework is useful to designers, offering an intuitive approach to inclusive design.The work presented in this article forms part of VERITAS, which is funded by the European Commission's 7th Framework Programme (FP7) (grant agreement # 247765 FP7-ICT-2009.7.2)