The Use of an AI-Driven Threat Detection System to Strengthen the Network Security of the College English Education Platform

Abstract

College English education has shifted more and more online platforms in the digital age, utilizing network-based resources for classroom simulations, interactive learning, and real-time evaluations. Dependence on network systems, particularly online platforms, poses significant security risks due to their handling of sensitive data like real-time communication and student data. Robust security measures have to be maintained to safeguard sensitive information and ensure continuous learning English education opportunities in light of the growing threat of cyber-attacks. This review explores the application of an Artificial Intelligence (AI) driven threat detection system to enhance the network security of a college English education platform. The system uses innovative Machine Learning (ML) model, and Deep Learning (DL), anomaly detection, and real-time threat analysis, to identify and prevent possible security breaches. It monitors network data continuously, analyzes behavioral patterns, and detects unusual activities that indicate a cyber-attack. With the integration of AI, the system improves the efficiency of threat detection and also reduces false positives, thereby enabling faster response times. This system's effectiveness in increasing platform security and protecting user data is proved through various types of performance assessments, indicating its potential to greatly improve the overall security posture of college instructional platforms.