CONTEXT-AWARE TRUST MODELING FOR SECURE WI-FI CLIENT AND ACCESS POINT SELECTION

Abstract

Wireless Fidelity (Wi-Fi) has become the dominant communication technology supporting mobile computing, enterprise networking, Internet of Things (IoT) applications, smart healthcare, industrial automation, intelligent transportation, and modern digital services. As wireless connectivity continues to expand, secure client association and access point selection have become increasingly important challenges for maintaining reliable communication and protecting network resources against malicious attacks. Conventional Wi-Fi client association mechanisms primarily rely on physical layer parameters such as received signal strength, channel quality, and transmission rate when selecting an access point. Although these parameters optimize communication performance, they do not adequately evaluate the trustworthiness or security posture of wireless devices participating in the association process. Consequently, attackers can exploit client association procedures through rogue access points, evil twin attacks, beacon spoofing, signal amplification, authentication manipulation, and deceptive service advertisements to attract legitimate clients and compromise wireless communications. This paper proposes a context-aware trust modeling framework for secure Wi-Fi client and access point selection that combines behavioral analysis, contextual information, historical communication patterns, and intelligent trust evaluation to improve wireless network security. The proposed framework continuously collects heterogeneous contextual information including client mobility, authentication history, access point reputation, signal consistency, communication stability, traffic behavior, channel utilization, encryption capability, device identity, and network service quality. The collected information is processed through data preprocessing, context extraction, trust computation, behavioral profiling, and intelligent decision-making to generate dynamic trust scores representing the reliability of both wireless clients and access points. The framework incorporates machine learningassisted trust analysis and multi-factor decision mechanisms to distinguish legitimate communication entities from potentially malicious participants before client association occurs. Experimental evaluation under representative enterprise wireless scenarios demonstrates that the proposed context-aware trust model significantly improves secure access point selection, reduces successful rogue access point associations, lowers false trust evaluations, and enhances wireless communication reliability compared with conventional signal strength-based association mechanisms. The proposed architecture provides a scalable and adaptive security framework suitable for enterprise wireless local area networks, industrial wireless infrastructures, smart city environments, healthcare systems, educational institutions, and next-generation intelligent wireless communication networks.