Abstract

The increasing complexity and sophistication of cyber threats necessitate robust and adaptive detection mechanisms capable of identifying anomalies in real-time. Traditional methods, relying solely on statistical analysis or computational intelligence, often face limitations such as high false-positive rates, delayed detection, or inability to adapt to evolving attack patterns. Hybrid approaches that integrate statistical techniques with computational methods, including machine learning and data mining, offer a promising solution for early anomaly detection in cybersecurity. Statistical models provide a framework for identifying deviations from expected network behavior, while computational methods enhance the system's ability to recognize subtle, non-linear, or evolving attack patterns. This paper explores the design, implementation, and evaluation of hybrid statistical–computational methods for early cyber anomaly detection. Key contributions include the identification of optimal statistical features, the integration of machine learning algorithms for classification and prediction, and a framework for evaluating detection accuracy and response time. The study demonstrates that hybrid methods outperform standalone approaches in terms of precision, recall, and detection latency, offering a more reliable and adaptive solution for securing critical information systems