Detection of Security and Privacy Attacks Disrupting User Immersive Experience in Virtual Reality Learning Environments
Detection of Security and Privacy Attacks Disrupting User Immersive Experience in Virtual Reality Learning Environments
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DOI：
10.1109/tsc.2022.3216539
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发表时间：
2023-07
影响因子：
8.1
通讯作者：
Samaikya Valluripally;Benjamin Frailey;Brady Kruse;Boonakij Palipatana;Roland Oruche;Aniket Gulhane;K. A. Hoque;P. Calyam
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文献类型：
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作者：
Samaikya Valluripally;Benjamin Frailey;Brady Kruse;Boonakij Palipatana;Roland Oruche;Aniket Gulhane;K. A. Hoque;P. Calyam
虚拟现实学习环境（VRLEs）是一种新型的沉浸式环境，它与可穿戴设备相结合，以协作方式在特殊教育、外科培训等领域提供远程学习内容。未经授权访问这些连接的设备可能会导致安全、隐私攻击（SP），从而对用户沉浸式体验（ux）产生不利影响。在本文中，我们识别了影响应用程序可用性和沉浸式体验的潜在SP攻击面，并提出了一种新的异常检测方法，以便在ux中断之前检测攻击。具体来说，我们应用：(i)机器学习技术，如多标签KNN分类算法来检测基于网络的攻击的异常事件，包括DoS（数据包篡改，数据包丢弃，数据包重复）的潜在威胁场景，以及（ii）统计分析技术，使用布尔和阈值函数（z分数）的组合来检测与基于应用程序的攻击相关的异常（未经授权访问）。我们使用VRLE应用案例研究（即vSocial）证明了我们提出的异常检测方法的有效性，vSocial是专门为教育有学习障碍的青少年有关社交线索和互动而设计的。根据我们的检测结果，我们验证了基于网络和应用程序的SP攻击对VRLE ux的影响。
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 a multi-label KNN classification algorithm to detect anomaly events of network-based attacks that include potential threat scenarios of DoS (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.