Tracking Attacks on Virtual Reality Systems Virtual reality (VR) is a rapidly advancing technology with diverse applications. VR systems offer an immersive, lifelike virtual experience by rendering interactive views on a headmounted display. VR systems are vulnerable to various types of attacks, which could have dire consequences as they are designed to replace our perception of the physical world. However, there have been few studies on the security and integrity issues of the VR systems. We focus on the popular HTC Vive VR system and propose novel attack methods on blocking and manipulating its tracking subsystem. It is shown that simple attacks can jam or even manipulate the entire position and pose tracking process. Possible countermeasures are suggested to make VR systems safer and more secure.

Physical Integrity Attack Detection of Surveillance Camera with Deep Learning based Video Frame Interpolation Surveillance cameras, which is a form of Cyber Physical System, are deployed extensively to provide visual surveillance monitoring of activities of interest or anomalies. However, these cameras are at risks of physical security attacks against their physical attributes or configuration like tampering of their recording coverage, camera positions or recording configurations like focus and zoom factors. Such adversarial alteration of physical configuration could also be invoked through cyber security attacks against the camerau0027s software vulnerabilities to administratively change the camerau0027s physical configuration settings. When such Cyber Physical attacks occur, they affect the integrity of the targeted cameras that would in turn render these cameras ineffective in fulfilling the intended security functions. There is a significant measure of research work in detection mechanisms of cyberattacks against these Cyber Physical devices, however it is understudied area with such mechanisms against integrity attacks on physical configuration. This research proposes the use of the novel use of deep learning algorithms to detect such physical attacks originating from cyber or physical spaces. Additionally, we proposed the novel use of deep learningbased video frame interpolation for such detection that has comparatively better performance to other anomaly detectors in spatiotemporal environments.