A Machine Learning-Based Zero Trust Security Framework for Predicting Cyberattacks on Examination Results

Abstract

Strong security measures must be developed to safeguard sensitive data, due to the increasing sophistication of cyberattacks targeting educational institutions. To anticipate and mitigate cyberattacks on systems handling examination results, this study proposes a zero trust security framework enhanced with machine learning approaches using the principle of “never trust, always verify,”. The zero trust model makes sure that every access request is thoroughly verified and approved before allowing access to resources. By incorporating machine learning algorithms into this framework, network traffic, user behavior, and access patterns can be continuously monitored and analyzed to detect and address possible threats instantly. The methodology adopted describes creating a zero trust security architecture based on machine learning to forecast cyberattacks on examination result outcomes. To assess the effectiveness of the suggested approach, extensive tests were carried out utilizing a dataset that includes historical cyberattack records and simulated attack scenarios. Response time, false positive rate, and detection accuracy are important evaluation measures that were engaged. Nonetheless, the findings show that the zero trust security framework, which is based on machine learning, greatly improves the detection and avoidance of assaults on systems that handle test results. Proactive threat management is made possible by the framework's predictive capabilities, which also guarantees the security and integrity of examination data while lowering the possibility of successful intrusions. By offering a flexible method for protecting educational information systems from cyberthreats, this research advances the subject of cybersecurity.