CYBER RISK ANALYSIS AND THREAT ASSESSMENTS FROM GOVERNMENT OPEN DATASETS ON EDUCATION USING HYBRID DEEP LEARNING MODEL

Abstract

Educational institutions are increasingly reliant on technology, making them prime targets for cyber attacks. This research proposes a novel approach to enhance cyber security in education by leveraging deep learning techniques to analyze government open data sets. The framework involves data collection, preprocessing, feature engineering, model training, and evaluation. By applying advanced machine learning algorithms, the study aimed to identify patterns, anomalies, and potential threats within the educational sector. Through rigorous experimentation and analysis, the research demonstrated the effectiveness of proposed approach in detecting and mitigating cyber risks. The findings of this research contribute to the development of robust cyber security strategies for educational institutions, safeguarding their digital assets and ensuring the continuity of educational services.