Abstract
This paper rigorously analyzes two principal methodologies in network traffic anomaly detection: feature detection and anomaly detection. Each methodology exhibits distinct strengths and confronts specific challenges. The study elucidates how the integration of deep learning with artificial immune systems could potentially transform feature detection. Moreover, it illustrates the enhancement of anomaly detection through the synthesis of machine learning techniques with traditional methods. Looking ahead, the paper delineates research trajectories that concentrate on merging deep learning, artificial intelligence, and behavioral analysis. This integration aims to augment the precision, efficiency, and adaptability of network anomaly traffic monitoring systems. Proposed future strategies include advanced methods in data preprocessing, model development, pattern recognition, and adaptive adjustments. These strategies are directed towards fortifying network defenses in response to the dynamically changing spectrum of cyber threats.
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