Review on Portal Intelligent Authentication Algorithm Based on Artificial Intelligence

Abstract

The Portal authentication system constitutes a core component of modern network authentication systems. The vast majority of conventional authentication schemes rely on static username-password authentication mechanisms, which inherently suffer from security vulnerabilities. Furthermore, mandatory periodic password updates and high password complexity requirements degrade user experience significantly. This paper delivers a comprehensive and integrated analysis of state-of-the-art intelligent authentication algorithms for network access authentication, and thoroughly dissects three technical implementation frameworks: user behavior modeling, anomalous access behavior detection, and authentication decision optimization. The research focuses on elaborating the fundamental principles and practical deployment details within real authentication systems for temporal behavior modeling based on Long Short-Term Memory (LSTM), integrated anomaly detection architecture, and decision optimization strategies. It systematically organizes the algorithm execution workflow and simulation testing schemes. Ultimately, this paper discusses and analyzes the prevailing technical bottlenecks and prospective development trends of high-performance intelligent authentication algorithms.