<![CDATA[Global cyber threats continue to increase along with the widespread digital transformation across various sectors. This study aims to predict the types of cyberattacks based on global historical data from 2015 to 2024. Data was obtained from the Global Cybersecurity Threats dataset, which includes information on countries, affected sectors, and types of attacks. The method used was supervised learning with the Random Forest algorithm, which is known to be effective for classifying and analyzing complex variables. The results show that this algorithm is capable of identifying attack patterns with high accuracy and assisting in early threat detection. This research is expected to contribute to the development of data-driven cybersecurity systems and predictive modeling. Global cyberthreats continue to grow in complexity, along with society's increasing reliance on digital systems. This study aims to analyze trends and predict the types of cyberthreats based on historical data from 2015 to 2024, obtained from the Global Cybersecurity Threats dataset. The method used was supervised learning with the Random Forest algorithm to classify attack types and predict potential financial losses. The analysis results show that the model can identify important patterns that can assist organizations in mitigating cyber risks. This research contributes to the development of data-driven cyber threat intelligence systems]]>
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