Network Security Assessment and Improvement Strategies in English Online Teaching Platforms

Abstract

The current English online teaching platforms have problems such as insufficient data encryption strength, weak identity authentication mechanisms and imperfect security strategies. To this end, this paper constructs a network security assessment framework adapted to the English online teaching platform. The distribution and type of system vulnerabilities are analyzed by Nessus scanning tools, and the severity of vulnerabilities is quantified by the Common Vulnerability Scoring System (CVSS). The Metasploit framework is used to simulate attack paths to evaluate the platform’s defense capabilities. At the same time, an anomaly detection model is constructed based on user behavior data, and K-Means and Isolation Forest (IF) algorithms are used to identify abnormal behaviors and comprehensively evaluate security risks. On this basis, improvement strategies are designed, including access control mechanisms based on Zero Trust Architecture (ZTA), multi-factor authentication (MFA) solutions, and dynamic permission allocation technology to strengthen user identity authentication; TLS 1.3 protocol and AES-256 encryption technology are used to optimize data transmission and storage security and improve the overall network security level of the platform. After fixing the SQL injection (SQLi) and remote code execution (RCE) vulnerabilities, the platform’s attack success rate drops from 85% and 92% to 10% and 12%, respectively; the accuracy of abnormal behavior detection increases from 85.2% to 94.2%; the false positive rate drops from 12.3% to 7.5%; the integrity of data transmission remains at 85% under extremely high attack intensity, significantly enhancing the overall security protection capability of the platform. The study shows that the improvement measures based on the above strategies effectively improve the network security level of the English online teaching platform and provide a scientific basis for the security optimization of similar platforms.