# Cybersecurity in the AI-Based Metaverse: A Survey
## 3 Security in Metaverse
### Security in Metaverse Based on Biometric Data  
A VR environment can be created with various type of visual stimuli and scenarios, which can be easily switched or repeated, while eye tracking shows exactly where the participant's attention is at any given moment of the experience and what visual elements trigger certain responses. In particular, VR experiences can be enhanced by eye tracking. According to [44], virtual reality devices store most of a user's personal information, such as account numbers and biometric information, so they are constantly at threat of being attacked by hackers. In addition, these attacks can damage the headset's vision. Many solutions have been proposed for authentication, but the most effective methods (e.g., PIN, pattern, biometric brain, and so on) require a significant amount of user time and are also highly insecure. Therefore, the authors of this study presented blinkey, a method for securing VR devices equipped with eye-tracking for user authentication. In this method, authentication is performed by blinking one's eyes according to a rhythm that is only known to the user. This is a new method of authentication that uses a passcode. Instead of numbers, letters, or characters, users blink to different rhythms. To evaluate their blinkey method, the authors discussed the effects of the following commonly seen attacks: zero-effort attack, statistical attack, shoulder-surfing attack, and credential-aware attack. Their experiments were conducted using two machine learning algorithms for classification: support vector machine (SVM) and \(k\)-nearest neighbors (\(k\)NN). They achieved an average error rate (ERR) of 4% using this method, making it effective against all types of attacks. Compared with commonly used methods on mobile devices (e.g., passwords, PINs, and pattern locks), blinkey exceeded user expectations from the perspectives of security and usability. Due to a special genetic pattern controlling the pupil's expansion and contraction, the accuracy and speed of the blinkey method are very high. In addition, the pupil size change between each blink is also as unique as a fingerprint. An artificial-intelligence-driven deepfake is a picture, video, or audio recording that looks real but is actually a fake. Deepfakes can be created by AI technologies such as autoencoders (artificial neural networks that reconstruct the input from simpler representations) and generative adversarial networks (GANs). Metaverse users can create their own hyper-real avatars using their biometric data. However, deepfakes pose many privacy and cybersecurity challenges. Based on a deep convolutional neural network, ref. [45] has proposed a lip-based speaker authentication system that defends against severe deep fake attacks. There are usually two types of deepfakes: (1) manipulation methods (e.g., face swapping) and lip syncing and (2) visual speaker authentication (VSA) systems are vulnerable to deepfake attacks, which mimic the original user's pronunciation. It has been shown that the lips of people can be used as a biometric feature to distinguish speakers. Lip-based authentication, which has high security and can be used in most systems, contains both fixed and mobile data related to identity (i.e., the shape and appearance of the lips as well as their movement). Using VSA, the authors proposed a deep learning algorithm that can detect deepfake attacks without prior manipulation knowledge. Due to the vulnerability of static information to deepfakes, they have used dynamic information for authentication. To extract an image of the lip area from a video of a face, the authors used the Dlib detector (a detector which is used to extract the lip region from a video of a face), and to verify the spoken text, they used connectionist temporal classification (CTC). The proposed network consists of two subnets: (1) a low-level fundamental lip feature extraction subnet (FFE-Net) and (2) a high-level representative lip feature extraction and classification subnet (RC-Net).