{"content":[{"text":"example, Mathis, Fawaz, and Khamis [10] developed an au- \n thentication method based on Guiard’s kinematic chain model \n to address the issue of bystanders inferring user input. In their \n method, the non-dominant handheld controller interacts with \n a cube attached to it in VR, while the dominant controller \n enters a set of PIN numbers displayed on the cube while in \n VR. This two way handed interaction technique makes it dif- \n ﬁcult for a bystander to infer user input. They further added \n a deep learning architecture for time series classiﬁcation to \n recognise a user’s legitimate behavioural biometric PIN input. \n Lu et al. [8] proposed the use of hand motion authentication \n with eye-free interaction in VR systems, while Li et al. [6] \n proposed the use of brain signal biometric authentication. \n Risk assessment and authentication-based prevention con- \n stitute a good start in VR cyber security, but do not address the \n need to detect whether a security breach has occurred when \n prevention fails, especially for new threats. Here, we take the \n ﬁrst step towards intrusion detection in VR cyber security. \n We recognise that it would be counterproductive to develop a \n method that would detect an attack after it has caused impact \n on the user, or that would generate erroneous warnings of \n attacks (false positives) that would adversely affect a user’s \n immersive experience. So, we evaluate our solution in terms \n of the time it takes to detect an attack and provide a simple \n suggestion on how to minimise false positives. To conduct \n our experimental evaluations, we have developed a virtual en- \n vironment designed to carry out a variety of cyber attacks and \n observe their impact in different conditions and locomotion \n techniques. Speciﬁcally, the contributions of this paper are:","bbox":{"left":0.087471,"top":0.091866,"width":0.395973,"height":0.43781400000000004}},{"text":"visual discomfort through disruption of user locomotion, inter- \n action, audio and visuals, as framerate disruption is a known \n major factor leading to VR sickness [17].","bbox":{"left":0.519005,"top":0.091866,"width":0.39546400000000004,"height":0.045347}},{"text":"2.1 The VR test-bed \n We have developed out testbed in the Unity 3D game engine, \n using the XR Interaction Toolkit for the mechanics and var- \n ious interaction techniques. We chose to use a Room-Scale \n XR Rig, which allows for six degrees of freedom movement. \n Locomotion was used as our main form of movement tech- \n nique enabling the user to move around using the Joystick on \n the controllers. The environment was designed as an ofﬁce \n space with objects placed at random locations, allowing the \n user to freely interact with them. To run our experiments, we \n used two VR-ready laptops (Alienware 51M: GeForce RTX \n 2060 and MSI GE66 Raider 10SGS: GeForce RTX 2070) and \n two VR headsets (Oculus Rift S and Oculus Quest 2).","bbox":{"left":0.518647,"top":0.157179,"width":0.39595900000000006,"height":0.20834199999999997}},{"text":"2.2 Cyber Attack 1: GPU-based attack \n Cyber attacks on GPUs have been previously shown, but not \n in relation to their impact on VR experience [12] [11] [15]. \n To implement our GPU-based attack, we created a malware \n using the OpenGL API, designed to assign long running tasks \n to the GPU, thereby affecting its availability to compute other \n graphics tasks. We created our malware in C++, delivered in \n the form of an executable ﬁle format (exe). Next, we created \n a series of images to load into the GPU via the malware. \n In our attack model, we assume that the target machine is \n already compromised and our payload is delivered using a \n post-exploitation tool, such as Meterpreter [1]. We conﬁgure \n the intensity of the malware’s attack based on the size of the \n image (in our case, 542 KB, 1.06 MB, 1.82 MB and 3.13 MB). \n A simple batch script was developed to automate the attack. \n The operation of the malware was hidden from the user using \n lines of code;","bbox":{"left":0.518825,"top":0.385487,"width":0.395795,"height":0.268721}},{"text":"• We devise a GPU-based attack involving a malware that \n uses the OpenGL application programming interface \n (API) to overwhelm the GPU resources.","bbox":{"left":0.106956,"top":0.539866,"width":0.373639,"height":0.045347000000000026}},{"text":"•","bbox":{"left":0.106956,"top":0.594281,"width":0.005697999999999995,"height":0.015158000000000005}},{"text":"We devise a network-based attack with the aim of caus- \n ing visual, interaction, locomotion and auditory disrup- \n tions within a collaborative VR environment.","bbox":{"left":0.120792,"top":0.594281,"width":0.3625,"height":0.045347000000000026}},{"text":"•","bbox":{"left":0.106956,"top":0.648696,"width":0.005697999999999995,"height":0.015158000000000005}},{"text":"We propose the ﬁrst VR intrusion detection system \n which utilises machine learning to detect an ongoing \n attack and warn the user before its impact escalates.","bbox":{"left":0.120208,"top":0.648696,"width":0.36038400000000004,"height":0.045347999999999944}},{"text":"HWND hWnd = GetConsoleWindow ( ) \n ShowWindow (hWnd , SW_HIDE) \n GLCall ( glfwHideWindow ( windowone ) )","bbox":{"left":0.5171,"top":0.662414,"width":0.32174500000000006,"height":0.045348000000000055}},{"text":"2","bbox":{"left":0.088235,"top":0.71302,"width":0.009766999999999998,"height":0.019638999999999962}},{"text":"Frame rate-oriented cyber attacks on user \n experience","bbox":{"left":0.117537,"top":0.71302,"width":0.363481,"height":0.037250000000000005}},{"text":"Figure 2 shows the environment in normal conditions where \n there is no visual discomfort (top) and during an attack (bot- \n tom), where the frame rate has dropped and extreme screen \n tearing is observed. Figure 1 further shows the mechanics \n of the impact of the missed and dropped frames on frame \n processing during an attack, which eventually leads to the VR \n system crashing. The default time window for a VR device to \n display images on the screen is 11 ms. VR uses reprojection, \n a technique that warps the rendered image before sending \n it to the display to correct for the head movement occurred \n after the rendering. This can reduce latency and help maintain \n frame rate. During the attack, frames take longer to render as \n a result of the GPU being overwhelmed. This results initially","bbox":{"left":0.519412,"top":0.716329,"width":0.39504800000000007,"height":0.19629700000000005}},{"text":"From a system perspective, attacks in VR may relate to the \n GPU, sensors and displays, which jointly determine the out- \n put, input and computing efﬁciency of a VR system, i.e. its \n depth of information [14]. In this paper, we speciﬁcally focus \n on GPU as it directly determines the frame rate and through \n it the user experience. As proof of concept, we also target \n the frame rate through a network denial of service attack, es- \n pecially as collaborative VR environments require network \n connection to function and typically network disruptions af- \n fect the frame rate. Both attacks aim at maliciously causing","bbox":{"left":0.088235,"top":0.761613,"width":0.395057,"height":0.15101300000000006}},{"text":"2","bbox":{"left":0.49593,"top":0.935205,"width":0.008139000000000007,"height":0.015158000000000005}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}