Evaluating the Effectiveness of Machine Learning in Cyber Threat Detection

In today's digital era, cyber threats pose significant challenges to organizations, necessitating more advanced detection methods. This study aims to evaluate the effectiveness of machine learning (ML) techniques in detecting cyber threats, focusing on supervised, unsupervised, and reinforcement learning models. Using datasets such as CICIDS2017, the study trains models including Random Forest, Support Vector Machines (SVM), and Neural Networks. The evaluation is based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that the Random Forest model outperforms others with an accuracy of 92.5\%, a precision of 91.8\%, and an F1-score of 92.4\%. This superior performance highlights its potential for real-time threat detection, as evidenced by a case study where the model effectively identified previously undetected cyber threats in a large technology company's network. However, the study also acknowledges challenges such as data quality and the need for continuous model updates. The findings suggest that integrating ML models into cybersecurity frameworks can significantly enhance threat detection efficiency. Future research should explore combining ML with traditional methods and improving model robustness against adversarial attacks to further advance cybersecurity measures.