A Context-Aware Risk Scoring and Adaptive Access Control Model for Secure Web3 Applications

Web3 apps offer decentralized and user-centric digital services through blockchain networks, smart contracts, and pseudonymous wallet identities. Nevertheless, the transparency and evolving nature of Web3 environments create security issues such as illicit transactions, credentials theft, wallet anomalies, and access risks based on contextual factors. Traditional access control methods typically depend on static identity, predetermined permissions, or fixed rules so they can be inefficient when the security status of a user or transaction changes instantly. This paper suggests a context-aware risk scoring and adaptive access control system to be used in secure Web3 applications. The proposed system relies on a range of contextual factors to calculate a dynamic risk score for each of the access requests or transactions. Risky requests are allowed, requests with medium-level risks are subjected to extra verification needs, while the requests categorized under high-level risk are turned down. For the model to recognize behavioral changes and security condition shifts, it uses continuous contextual evaluation in the middle of active sessions. The aim of the experimental evaluation is to assess the capability of the system to detect attacks and determine its rate of false positive results, the effectiveness of its classification, the latency of its decision-making, and the improvement of security.