Virtual Reality Learning Environments (VRLEs) are a new form of immersive environments which are integrated with wearable devices for delivering distance learning content in a collaborative manner in e.g.,special education,surgical training. Gaining unauthorized access to these connected devices can cause security, privacy attacks (SP) that adversely impacts the user immersive experience (UIX). In this article, we identify potential SP attack surfaces that impact the application usability and immersion experience, and propose a novel anomaly detection method to detect attacks before the UIX can be disrupted. Specifically, we apply: (i) machine learning techniques such as amulti-label KNN classificationalgorithm to detect anomaly events of network-based attacks that include potential threat scenarios ofDoS (packet tampering, packet drop, packet duplication), and (ii) statistical analysis techniques that use a combination of boolean and threshold functions (Z-scores) to detect an anomaly related to application-based attacks (Unauthorized access). We demonstrate the effectiveness of our proposed anomaly detection method using a VRLE application case study viz., vSocial, specifically designed for teaching youth with learning impediments about social cues and interactions. Based on our detection results, we validate the impact of network and application based SP attacks on the VRLE UIX.

Social Virtual Reality Learning Environments (VRLE) offer a new medium for flexible and immersive learning environments with geo-distributed users. Ensuring user safety in VRLE application domains such as education, flight simulations, military training is of utmost importance. Specifically, there is a need to study the impact of “immersion attacks” (e.g., chaperone attack, occlusion) and other types of attacks/faults (e.g., unauthorized access, network congestion) that may cause user safety issues (i.e., inducing ofcybersickness). In this article, we present a novel framework to quantify the security, privacy issues triggered via immersion attacks and other types of attacks/faults. By using a real-world social VRLE viz., vSocial and creating a novel attack-fault tree model, we show that such attacks can induce undesirable levels of cybersickness. Next, we convert these attack-fault trees into stochastic timed automata (STA) representations to perform statistical model checking for a given attacker profile. Using this model checking approach, we determine the most vulnerable threat scenarios that can trigger high occurrence cases of cybersickness for VRLE users. Lastly, we show the effectiveness of our attack-fault tree modeling by incorporating suitable design principles such ashardening,diversity,redundancyandprinciple of least privilegeto ensure user safety in a VRLE session.

Delivering curriculum using desktop-based virtual learning environment (VLE) technologies in a collaborative group setting has been shown to reduce the social skill limitations of students with learning disabilities. However, the lack of the immersiveness and effective generalization of acquiring knowledge and skills among students remains a critical challenge in the interactive tools used in current VLEs. In this paper, we present a networked social virtual reality learning environment (VRLE) system viz., vSocial that has been redesigned based on iterative user feedback and developed in order to leverage the latest advances in integration of smart devices such as VR headsets for virtual content delivery. We describe a comparative study to evaluate technology trade-offs in the development process of transitioning from a VLE to a VRLE, from both technological and user (e.g., student/instructor) perspectives. Lastly, we outline open issues in using VRLEs which include: system complexity, emotion recognition, cybersickness and system sustainability.

Social Virtual Reality Learning Environment (VRLE) is a novel edge computing platform for collaboration amongst distributed users. Given that VRLEs are used for critical applications (e.g., special education, public safety training), it is important to ensure security and privacy issues. In this paper, we present a novel framework to obtain quantitative assessments of threats and vulnerabilities for VRLEs. Based on the use cases from an actual social VRLE viz., vSocial, we first model the security and privacy using the attack trees. Subsequently, these attack trees are converted into stochastic timed automata representations that allow for rigorous statistical model checking. Such an analysis helps us adopt pertinent design principles such as hardening, diversity and principle of least privilege to enhance the resilience of social VRLEs. Through experiments in a vSocial case study, we demonstrate the effectiveness of our attack tree modeling with a reduction of 26% in probability of loss of integrity (security) and 80% in privacy leakage (privacy) in before and after scenarios pertaining to the adoption of the design principles.

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.