Self-learning model fusion for network anomaly detection: A hybrid CNN-LSTM-transformer framework.

ABSTRACT: The rapid evolution of cyber threats poses significant challenges to the adaptability and performance of anomaly detection systems. This study presents an innovative hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models with a novel self-learning mechanism to enhance network traffic anomaly detection. Our key contributions include: (1) a synergistic two-stage model fusion architecture that captures both spatial and temporal traffic patterns; (2) an adaptive learning mechanism with multi-metric drift detection that autonomously responds to evolving threats; and (3) a knowledge preservation strategy that maintains detection capabilities while adapting to new attack patterns. The proposed CNN-LSTM mod

SUBMITTER: Wang J 

PROVIDER: S-EPMC12571298 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature