Integrating Network Security and English Education Through Artificial Intelligence in 6G Environment

As digital technologies advance rapidly, the integration of English education and network security has become increasingly important, particularly in next‐generation 6G networks. Sophisticated cyberattacks threaten the reliability and integrity of English online education platforms, reducing trust among educators and learners. To address this issue, this study proposes an integrated framework that combines network security and English language education using artificial intelligence (AI) and deep learning techniques within a 6G communication environment. The proposed system introduces a Monarch Butterfly fine‐tuned Attention‐based Bidirectional Long Short‐Term Memory (MB‐AttBiLSTM) model to detect abnormal network traffic and strengthen the security of English educational platforms. The attention mechanism highlights important data features related to learning systems, improving the detection of context‐sensitive anomalies. Meanwhile, the BiLSTM component captures temporal dependencies in network traffic, enabling effective identification of sequential attack patterns. Network traffic data, including both normal and malicious activities, is collected from English online education platforms. The data is cleaned to remove inconsistencies and standardized using Z‐score normalization. Principal Component Analysis (PCA) is then applied for feature extraction and dimensionality reduction. Experimental results show that the proposed model achieves 98.5% accuracy, ensuring secure and reliable English online learning in a 6G environment.