Blockchain-Based Social Networking Model Empowered by Non-Fungible Tokens

Abstract

The increasing reliance on the internet has escalated the frequency and sophistication of cyber threats, making timely identification and mitigation essential. This research presents an AI-powered framework for cyber threat detection and profiling using Natural Language Processing (NLP) and Machine Learning (ML) techniques. By utilizing Twitter as an Open Source Intelligence (OSINT) platform, the system collects real-time threat intelligence, classifies threats, and maps them to the MITRE ATT&CK framework to provide actionable insights. Key processes include data preprocessing, feature extraction using advanced NLP models, and threat profiling to assess intent, origins, and potential impacts. The automated approach reduces analyst workload, enhances accuracy, and accelerates response times, addressing the limitations of manual threat analysis and noisy data sources. This framework aims to advance proactive cybersecurity by delivering real-time, context-aware threat intelligence. Keywords: Blockchain, Non-Fungible Tokens(NFTS), InterPlanetary File System(IPFS), Social Networking, Online Social Networks, Decentralization, Data Ownership, Content Monetization, Reputation Sytsem, NFT