ABSTRACT
The increasing complexity and frequency of network-based attacks have made effective anomaly detection systems essential for protecting modern networks from threats such as denial-of-service and sophisticated intrusions. Leveraging advancements in machine learning and deep learning, these systems aim to accurately differentiate malicious activities from normal traffic. This paper examines current approaches to network anomaly detection, combining traditional statistical methods with advanced models, including ensemble learning, feature engineering, and deep neural networks, to handle large and complex datasets. By evaluating multiple algorithms based on accuracy, scalability, and computational efficiency, the study identifies effective strategies for developing robust and adaptive systems to combat evolving cyber threats.
