Metaverse intrusion detection of wormhole attacks based on a novel statistical mechanism

Abstract

The Metaverse shows great potential to facilitate the development of new technologies. Because the security of the Metaverse has attracted considerable attention, the automatic detection of malware in different scenarios related to the Metaverse has become significant as well. A Metaverse-based wireless system comprises various physical and virtual sensing models, and the security between these sensors and nodes should be further considered. A wormhole link is created by two malicious radio transceivers connected by high capacity out-of-band wireless or wired links. Wormhole attacks can easily subvert many network protocols and maliciously collect a large amount of traffic. As many new mobile applications in the Internet of Things (IoT) have emerged recently, the threat posed by wormhole attacks has increased. Accordingly, research efforts have been made to develop countermeasures for wormhole attacks. However, most of them have considered a static network, assuming that a wormhole does not move and that wormhole nodes cannot adaptively turn their radio on or off to avoid being detected. These assumptions limit the use of existing detection methods. Hence, we first study the security impact and characteristics of wormhole attacks in mobile cloud and Metaverse environments and find the possibility of matching statistical methods such as the sequential probability ratio test (SPRT) to detect wormholes. Moreover, in our assumed topology, nodes in the network have mobility. From our investigation results, we attempt to derive a novel defense mechanism design against wormhole attacks.