SaTC: CORE: Small: Modeling and Defense of Cyber Attacks for Improving Social Virtual Reality Resilience

社交虚拟现实（VR）学习环境（VRLE）技术为具有地理分布用户的灵活学习环境提供了一种新的媒介。安全的社交VRLE可以在广泛的领域提高人类的表现，包括特殊教育，灾难应对培训和医疗保健。该项目的目标是调查部署在网络系统上的社交VRLE，这些系统需要适当设计以防止独特的网络攻击。不合适的设计的后果使得社交VRLE容易受到安全漏洞和隐私泄露攻击，这些攻击严重影响用户（例如，STEM教育学生、急救人员、患者）。更严重的是，糟糕的设计以及安全和隐私攻击可能会导致用户沉浸式体验的中断。因此，项目活动的重点是确保社会VRLE的安全，隐私和安全（SPS），以实现安全和有效的学生学习活动。该项目的成果包括理论和技术防御攻击的社会VRLEs。这些成果将通过开源代码、教育材料、同行评审的出版物、设计案例研究和数据集传播给更广泛的社区，以促进建立值得信赖的学习环境，并为地理上分布的用户和VR设备提供合作环境。 为了实现上述目标，该项目旨在创建一个社会VRLE的学习和弹性方面的变革性共同设计。项目活动有助于解决在理解和定量测量网络攻击如何影响阻碍不间断用户沉浸式体验的系统和网络因素方面的知识差距。这些攻击的示例包括沉浸式攻击，例如遮挡攻击、伴侣文件攻击和网络故障场景。更具体地说，该项目的目标是：（一）调查技术的正式建模和可扩展的分析SPS的威胁/故障建模以及分析它们的相互依赖性，启发攻击故障树（AFT）。这项调查将包括AFT方面的新型威胁模型和用于AFT正式分析的可扩展技术;（ii）收集社交VRLE部署中网络攻击的威胁情报，以便在用户安全受到损害之前实时检测关键异常事件。这将导致基于机器学习模型和统计分析技术的新型异常检测技术，用于检测网络攻击（单一和多攻击场景）。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Social Virtual Reality (VR) Learning Environment (VRLE) technologies offer a new medium for flexible learning environments with geo-distributed users. Secure Social VRLEs can augment human performance in a wide range of domains including special education, disaster response training, and healthcare. This project’s goal is to investigate Social VRLEs deployed on networked systems that need to be properly designed for performance and resilience to prevent unique cyber-attacks. The consequences of ill-suited design make Social VRLEs vulnerable to security breaches and privacy leakage attacks that significantly impact users (e.g., STEM education students, first responders, patients). More severely, poor design as well as security and privacy attacks can cause disruption of user immersive experience. Hence, the project activities are focused on ensuring the security, privacy, and safety (SPS) in social VRLEs to enable safe and effective student learning activities. The project outcomes include theory and techniques for defense of attacks in social VRLEs. These outcomes will be disseminated to the broader community via open-source code, educational materials, peer-reviewed publications, design case studies, and data sets to foster the setup of trustworthy learning environments with a co-operative setting of geographically distributed users and VR devices. Towards meeting the above goal, this project aims to create a transformative co-design of learning and resilient aspects of social VRLEs. Project activities help address the knowledge gap in the understanding and quantitative measurement of how cyber-attacks influence the system and network factors that hinder the uninterrupted user immersive experience. Examples of these attacks include immersion attacks such as occlusion attack, Chaperone file attack and network fault scenarios. More specifically, the project objectives are: (i) to investigate techniques for formal modeling and scalable analysis of SPS threats/faults to model as well as analyze their inter-dependencies, inspired by attack-fault trees (AFTs). This investigation will include novel threat models in terms of AFTs and scalable techniques for formal analysis of AFTs; (ii) to collect threat intelligence on cyber-attacks in social VRLE deployments to detect critical anomaly events in real-time before the user safety gets compromised. This will lead to novel anomaly detection techniques based on machine learning models and statistical analysis techniques for the detection of cyber-attacks (single and multi-attack scenarios). The research outcomes validation features a real-world social VRLE that is hosted on available public cloud platforms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.