Network Traffic Image Analysis for Adaptive Botnet Detection

The patent replaces manual mechanical analysis by security experts with an automated image processing system. Network traffic data is converted into images using visualization techniques, and deep learning models automatically analyze these images to detect abnormal traffic patterns, eliminating the need for manual feature analysis while maintaining high detection accuracy and adaptability to new malware behaviors

The patent transforms network traffic parameters into visual image parameters. Traffic data such as packet sizes, timestamps, and protocol information are converted into image attributes like pixel intensity, color, and spatial distribution. This parameter transformation enables the use of deep learning models that can automatically adapt to changing malware behaviors through training on visual patterns rather than requiring manual rule updates

The system performs self-service through automatic learning and adaptation. The deep learning model continuously trains on new network traffic data, automatically learning patterns of emerging malware behaviors without requiring human intervention. The model self-adjusts its feature extraction and classification capabilities to keep pace with malware evolution, maintaining high detection precision while adapting to new threats

The patent implements feedback mechanisms where detection results and newly identified malware patterns are fed back into the system for retraining. This feedback loop enables the model to continuously improve its detection precision and adapt to changing malware features, creating a dynamic system that evolves alongside threat actors