CyberTwin: An AI-Powered Digital Twin Framework for Proactive Personal Cybersecurity

Modern digital life requires individuals to navigate an expansive and fragmented cyber landscape composed of heterogeneous online accounts, cloud services, interconnected mobile endpoints, and smart IoT devices. Despite escalating threats—including automated credential stuffing, sophisticated phishing campaigns, zero-day endpoint vulnerabilities, and passive digital footprint accumulation—contemporary personal cybersecurity relies predominantly on fragmented, reactive utility tools such as isolated password vaults, signature-based desktop antivirus engines, and sporadic breach lookup portals. These disconnected point solutions lack holistic contextual awareness and fail to furnish unified visibility into an individual's compounding cyber exposure. In this research, we propose CyberTwin: a unified, intelligent digital twin framework engineered specifically for personal cyber safety. CyberTwin synthesizes continuous asset telemetry, multi-vector threat detection, and explainable behavioral nudges to construct a synchronized, virtual representation of a user's digital ecosystem. Architected with a modular, high-concurrency web technology stack comprising a React.js and Tailwind CSS user interface, an asynchronous Python FastAPI backend, Pydantic v2 data validation schemas, SQLAlchemy ORM, and a centralized MySQL persistence layer, the platform incorporates a two-tier cascaded threat engine fusing a 1D-CNN+Bi-GRU deep neural network with large language model (LLM) contextual reasoning, a real-time lexical URL analyzer, and an Analytic Hierarchy Process (AHP)-grounded personal risk diagnostic model. Security is enforced through JSON Web Token (JWT) role authorization, salted bcrypt hashing, and TLS communication. Benchmarked across a comprehensive empirical testbed of 500 ground-truth threat vectors (Nazario, PhishTank, Enron) and simulated identity topologies, CyberTwin demonstrated a 68% acceleration in threat detection cycle times, sub-125 ms API service response latency, and a 91.2% overall satisfaction rating in user evaluations.