With the development of the metaverse, its position in cyberspace is becoming increasingly important. Due to its complexity and openness, the metaverse also faces many security threats and vulnerabilities. This paper focuses on the security vulnerability of the metaverse in cyberspace. First, the basic concepts and characteristics of the metaverse are introduced. Then, the architecture of the metaverse is hierarchically introduced, and the security vulnerability of the metaverse in cyberspace is analyzed. Furthermore, the security countermeasures of the metaverse in cyberspace are introduced. Finally, the importance of the metaverse is summarized and the corresponding security countermeasures in the future are discussed. This paper explores the security vulnerability of the metaverse in cyberspace from several technical perspectives and provides strong theoretical support and practical guidance for the development of the metaverse.
Fuzzing has become one of the most commonly used methods for exploiting software program vulnerabilities because of its ease of use and effectiveness. With the increasing complexity and importance of network services, fuzzing in parallel mode can greatly improve the efficiency of finding vulnerabilities. Typical representatives are Google's Clusterfuzz and the parallel mode of fuzzers such as AFL, Libfuzz. However, due to the lack of effective control in the process of mutating seeds in different fuzzing instances, the existing parallel fuzzing methods have problems such as high redundancy of generated test cases and low comprehensive coverage. In response to this problem, we proposes a parallel fuzzing method based on two-stage mutation. First, we use random mutations to perform fuzzing test within a period of time, divide the detected state space of the target program into independent state subspaces, and then distribute the corresponding tasks to each fuzzing instance, and use the same ordered mutation on each instance to ensure the generated test cases are lowredundant. We use this method to conduct experiments on the LAVA-M test set and open source software OpenJPEG. The experimental results show that it can improve the efficiency of fuzzing and find more crashes.
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