The Metaverse, like the Internet, is vulnerable to security threats, and discussing its future necessitates addressing cyber security issues. However, tackling threats in a virtual environment can be both difficult and costly. The security concerns in the Metaverse are diverse, ranging from money laundering and art forgery to personal data theft and avatar ownership. As biometric data becomes more prevalent in verifying users’ identities, it poses a significant cyber security threat. This blog aims to classify the existing cybersecurity in metaverse and their challenges into various categories with valuable insights based on the recent research by experts in the field.

AI in Metaverse

Artificial Intelligence (AI) plays a crucial role in all layers of the Metaverse, which consists of common infrastructures, protocols, and standards that are connected to the physical world via intermediaries. In this digital world, AI enables large-scale and continuous content production, and enhances user experience through speech recognition, machine vision, and natural language processing. AI also facilitates education and training by providing realistic virtual environments and virtual teaching assistants. As the Metaverse continues to evolve, the possibilities for collaboration between AI and Metaverse are endless.

Cybersecurity in Metaverse: A Context

The Metaverse, a virtual world that merges physical reality and digital virtual reality, relies on three fundamental technologies: immersive technologies, artificial intelligence, and blockchain. As companies invest heavily in developing an artificially intelligent Metaverse, the importance of cybersecurity has become increasingly critical. Despite the exponential increase in cybercrime, there is a lack of comprehensive research on AI-based Metaverse security. With a growing number of distributed denial-of-service attacks and user identification information theft, it is crucial to conduct inclusive and thorough research to identify vulnerabilities and weaknesses in the Metaverse. This article summarizes existing research on AI-based Metaverse cybersecurity and addresses relevant security challenges. The studies reveal that user identification plays a vital role in Metaverse security, with biometric methods being the most widely used. While biometric data is considered the safest method due to its uniqueness, it is also susceptible to misuse. An AI-based cyber-situation management system should be able to analyze large volumes of data with the aid of algorithms. This article serves as a comprehensive guide for future researchers in preparing them for the topic of Metaverse security based on artificial intelligence.

Cybersecurity in Metaverse Based on Biometric Data

Virtual Reality (VR) environments can be created using various visual stimuli and scenarios that can be easily changed or repeated. Eye tracking can enhance VR experiences by showing the participant’s attention at any given moment and identifying the visual elements that trigger specific responses. However, VR devices store personal information that can be attacked by hackers, and these attacks can damage the headset’s vision.

Traditional authentication methods, such as PINs and patterns, are highly insecure and time-consuming. Therefore, a new method called “blinkey” was proposed for user authentication. This method uses the unique pattern of the pupil’s expansion and contraction and is performed by blinking the eyes according to a rhythm known only to the user. The blinkey method achieved an average error rate of 4% against all types of attacks and exceeded user expectations from the perspectives of security and usability.

Deepfakes, created by AI technologies such as autoencoders and generative adversarial networks, can pose privacy and cybersecurity challenges. Lip-based speaker authentication, using both fixed and mobile data related to identity, can be used as a biometric feature to distinguish speakers. A deep learning algorithm was proposed to detect deepfake attacks by extracting dynamic information for authentication. The speaker authentication network based on dynamic talking habit performed better than other detection methods, especially in detecting fake videos produced by Faceswap-GAN.

Finally, social networks pose a serious threat as online photos can be used to retrieve features from a user’s face. Face recognition tools should be strong and efficient enough to create security for users. A face authentication system was attacked, and the models of the user’s face were created using images from social networks, which weakened the security of the system. VR-based spoofing is one of the most common attacks on face authentication systems, which can be vulnerable to these attacks. To counter these attacks, the authors used high-fidelity pose and expression normalization (HPEN) to extract 68 2D facial landmarks using the supervised descent method (SDM).

Cybersecurity in Metaverse Based on Transportation Data

Drones, also known as unmanned aerial vehicles (UAVs), are being used in a variety of industries, including the Metaverse. Several companies, such as Walmart, Google-owned Wing, Magellan Health, and Brinker International, are experimenting with drone deliveries. Drone Orange is constructing a large Metaverse platform in South Korea that relies on drones to collect all images and data. Drones have become increasingly significant in the Metaverse and require AI algorithms to operate. However, privacy issues, identity theft, and security concerns are sub-challenges that need to be addressed.

A fog-assisted Internet of Drones (IoD) has been proposed to analyze the vast amounts of data transferred by drones in the IoD. Federated learning (FL) has been proposed to protect drone privacy, but eavesdropping remains a concern. To improve system security, an algorithm was designed to control the transmission power of drones. The PCSF algorithm counts all FL times, optimizes wireless transmission power, and selects the best FL time and optimal power control method.

Results indicate that the PCSF algorithm performed best in terms of battery capacity, security rate, and FL training time. Vehicles such as planes, trains, trucks, and cars will soon be computer-based and capable of interacting with the Metaverse. Sensors enable data transmission, but are prone to hacking. The authors of a study examined the issues surrounding safe data transmission between sensors and addressed the problem through a Stackelberg game optimization problem based on a single-antenna and multi-antenna model. A jammer can lower the overall power usage of a cyber-physical transportation system (CPTS) and prevent eavesdropping assaults in addition to interfering with sensor and controller communications. The authors constructed two different kinds of communication methods: a single-antenna sensor, in which the information is conveyed in a single channel, and a multi-antenna sensor, in which the information is separated.

Cybersecurity in Metaverse Based on Virtual Learning

Incorporating a virtual world in an educational setting is an innovative use of the Metaverse that has been studied for its viability as a digital tool for teaching and learning. The flexibility of accessing synchronous and asynchronous information in a university context through technological means provides an alternative method of knowledge transmission and acquisition. A study on social virtual reality-based learning environments (VRLEs) was conducted to evaluate their security, privacy, and safety concerns, especially for young people with autism spectrum disorder (ASD).

The study proposed a risk assessment method and presented three attack scenarios that could negatively impact the content and learning results. The authors used a SecurITree tool to create an attack tree, which helped them to determine the probability of occurrence of each threat and its impact, which they used to calculate a risk score. Based on the risk score associated with a threat, creating a defensive strategy for the system became easier. The users connected to the virtual classroom using head-mounted display (HMD) devices through a cloud-based application hosted on the global environment for network innovations (GENI).

The study revealed that any upload speed below 30 Kbps resulted in high-fidelity crashing, but the frame rate was not significantly affected. The study also simulated packet sniffing attacks and demonstrated that avatar information, confidential host information, and server details were completely exposed, implying that all user information could be captured and deciphered. Finally, the authors developed a new framework for security and privacy that employed vSocial and a new attack-fault tree (AFT) to demonstrate the outcomes of cyber-attacks. They converted AFTs into stochastic timed automata (STA) presentations and demonstrated how their attack-fault tree model compounds suitable design fundamentals such as hardening, diversity, redundancy, and the principle of least privilege to ensure user safety.

Cybersecurity in Metaverse Based on Other Data

he use of cryptocurrency in the Metaverse encourages the holding of digital assets and the conduct of daily transactions using digital tokens. However, the use of cryptocurrencies has led to increased security problems, such as the difficulty of tracing transactions and the rise of illegal activities such as money laundering in the Bitcoin system. Researchers have proposed a feature-based framework to prevent Bitcoin money laundering by identifying statistical features at three levels: networks, accounts, and transactions. They used temporal directed transaction networks to analyze transaction records and proposed ATH motifs for the TAIN to analyze the complicated dynamic processes in the Bitcoin transaction network. They used logistic regression as a classifier to evaluate the performance of the model. They found that the PU learning framework performed better for Bitcoin mixing detection on extremely imbalanced data sets.

In the Metaverse, privacy and security are critical considerations, especially with the use of devices like headsets, smart glasses, and haptics. Protecting the vast amount of data gathered by these tools is essential as they are vulnerable to attacks. Researchers have presented a study on countermeasures to Sybil attacks on RPL-based networks in XR technology. They analyzed two countermeasures, namely, the Gini model and the ABC model, and found that the ABC model’s detection performance was inferior to that of Gini, but it had superior performance in terms of the average expected time. When designing a model to prevent Sybil attacks, detection delay and routing stability should be equally taken into account.

While the Metaverse relies on many cloud technologies to function, its ability to operate successfully is also dependent on physical world events. Edge computing can be used to enhance the safety of the Metaverse by distributing infrastructure closer to the end user and moving the compute, store, and network closer to the edge of the network. This helps to reduce latency and improve response times. In addition, edge computing can provide greater security and privacy by processing data closer to the source and reducing the need to transmit sensitive information over long distances.

One of the key challenges in edge computing for the Metaverse is resource management, which involves balancing the use of available computing resources with the requirements of the applications and services being deployed. In order to address this challenge, the authors proposed a resource management framework that uses a combination of dynamic resource allocation and load balancing techniques. The framework is based on a hierarchical structure, with edge nodes responsible for managing resources at the local level, and a central management system responsible for coordinating resource allocation and load balancing across the entire edge computing network.

The proposed framework was evaluated using a simulation model based on a real-world scenario involving a virtual reality application deployed in the Metaverse. The simulation results showed that the framework was able to improve application performance and reduce resource consumption compared to traditional resource management techniques. In addition, the framework was shown to be scalable and able to adapt to changing application requirements and network conditions.

Overall, the use of edge computing in the Metaverse has the potential to provide significant benefits in terms of performance, security, and privacy. However, it also presents significant challenges related to resource management, security, and network integration. Addressing these challenges will require a combination of technical solutions and new approaches to managing and securing the infrastructure that underpins the Metaverse.

AI-based Metaverse cyber security summary

The table below summarized the various techniques to enable cybersecurity in metaverse and offers summary of the above discussion.

In Conclusion…

This blog explored and evaluated the cybersecurity of the AI-based Metaverse by analyzing recent research references provided in Cybersecurity in the AI-Based Metaverse: A Survey by Mitra Pooyandeh, Ki-Jin Han & Insoo Sohn. An attempt is made to summarize the concepts from the best papers and cover various aspects of Metaverse applications to make the findings available to the readers. As highlighted, authentication is a major concern in cybersecurity, and biometric methods are commonly used for this purpose. Although biometric data is unique and hard to cheat, there are concerns about error detection and potential misuse. The blog also examined the role of AI in cybersecurity, and concluded that neural network algorithms can improve attack detection accuracy.

However, hackers can also use AI to attack the Metaverse. The lack of access to specific Metaverse data is a significant challenge for researchers in this field. Furthermore, without large volumes of data, AI systems may produce inaccurate results and false positives, which could have catastrophic consequences if data manipulation goes undetected. To develop AI-based Metaverse cybersecurity, collecting and classifying data is a crucial step as implicit intelligence algorithms require extensive data sets.

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Also read, Open Metaverse vs Meta (Facebook Metaverse): Which One Is For Greater Good?

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