Proactive Security Approaches for Web 3.0 and IoT Networks through SLM Based Chaos

The emergence of Web 3.0, which emphasizes decentralization and interoperability, presents new and complex security challenges that demand thorough examination. It is crucial to tackle the security concerns linked to the development of the Semantic Web in this particular situation. Blockchain technology, which is a fundamental aspect of Web 3.0, brings about security problems due to the implementation of decentralization. Although blockchain improves transparency and resistance to tampering, it is not impervious to weaknesses. Specific security issues that need to be addressed to ensure the strength of decentralized systems include smart contract vulnerabilities, weaknesses in consensus algorithms, and the potential for 51% assaults on decentralized networks. A significant obstacle faced by such networks is the elevated Peak to Average Power Ratio (PAPR), which diminishes their level of security. A high peak-to-average power ratio (PAPR) results in a significant amount of detectability and compromised security. Therefore, it is imperative to decrease the peak-to-average power ratio (PAPR) of the systems. Various methods have been utilized thus far to decrease the Peak-to-Average Power Ratio (PAPR) in systems. The proposed work uses a modified selective mapping technique and attains lower PAPR compared to previously existing work.