An Efficient SuperHyperSoft Framework for Evaluating LLMs-based Secure Blockchain Platforms

In the age of modern technology, the use of the internet has become imperative. However, this widespread access presents a double-edged sword and opens doors for hackers and scammers to exploit vulnerabilities and engage in illegal activities. Accordingly, scholars and stakeholders are attempting to solve this matter. Large Language Models (LLMs) have provided highly effective methodologies and solutions in various cybersecurity sectors. Hence, we exhibited the efficacy of LLMs in several information and communication technologies (ICT) such as the Internet of Things (IoT), cloud computing, blockchain technology (BCT)…etc which are attacked and threatened. Accordingly, the objective of our study is to illustrate how LLMs are supporting ICT, especially BC to be secure against attacks. Another study’s objective is to aid the stakeholders and enterprises that seek resilience and sustainability by recommending the most secure BC platform to adopt in critical sectors. Wherein, LLMs support BC in many directions as developing secure smart contracts and scanning the smart contract to protect it from any subversive acts by identifying anomalous activities. Hence, we suggested a soft opting model to rank the alternatives of BC platforms and recommend optimal BC. Also, the process of constructing this model requires leveraging several techniques. We applied for the first time SuperHyperSoft (SHS) as an extension of Hypersoft to treat various attributes and sub-attributes for BC based on LLMs. Multi-criteria decision-making (MCDM)techniques are utilized for their ability to treat conflicting sub-attributes. Hence, entropy and multi-objective optimization based on simple ratio analysis (MOOSRA) are utilized as techniques of MCDM. These techniques are working under the authority of the Single Value Neutrosophic (SVN) technique to support MCDM techniques in ambiguous situations