[PERSON] and colleagues [15] introduced AutoAttacker, a large language model-guided system for automatic cyberattack detection; yet, synchronizing information remains a challenge. Furthermore, the attention mechanism [16], an important technique in deep learning, allows models to \"focus\" on different parts by assigning different \"attention\" to them, thereby enhancing the model's ability to focus on the most relevant information in text and improving the ability to process complex language features. For example, [PERSON] and colleagues [17] proposed a financial system attack detection model called Finsformer based on a transformer and synchronized attention mechanism. The experimental results showed that their model significantly outperformed traditional models such as RNN and LSTM, achieving an accuracy of 97%; however, the model's computational efficiency was low, affecting accuracy and inference speed when dealing with large datasets. [PERSON] and colleagues [18] combined transformer and expert systems and evaluated the resulting model on real phishing websites, showing that even after three months, it maintained an accuracy of 96%; however, deploying the model on edge devices posed a challenge. The difficulty of detecting network attack behaviors lies in the need to accurately understand the meaning of texts and their context, especially when attack methods use language with obscure or double meaning. Additionally, the diversity and complexity of network attack behaviors demand highly flexible and adaptable detection methods. To address these challenges, we utilized the latest large language models as a foundation, leveraging their ability to pretrain on large-scale text data in order to capture subtle semantic differences and rich contextual information. Concurrently, the introduction of synchronized attention mechanisms enables models to adaptively focus on the most critical information for attack behavior detection, thereby enhancing the accuracy and efficiency of detection. In this context, the present paper proposes a novel approach that combines large language models with synchronized attention mechanisms. This approach is specifically designed to enhance accuracy and efficiency when detecting cyberattack behaviors. This innovative integration marks the first time that large language models have been coupled with synchronized attention mechanisms, providing a robust solution for identifying a wide range of complex and covert attack behaviors more effectively than traditional methods. The synchronized attention mechanism is a key innovation that enables a dynamic and holistic integration of data features across different layers of the language model, facilitating a deeper understanding of complex interactions within data. This approach significantly enhances the ability to detect nuanced or obscured cyberthreats. Additionally, the introduction of a synchronized loss function further optimizes our model's ability to handle multi-source data, significantly improving detection capabilities across diverse cyber-environments such as server logs, financial transactions, and social media interactions. Extensive experimental validation across these varied datasets demonstrates the superior performance of our method over existing models, underscoring its effectiveness in practical applications. This introduction sets the stage with a background on cybersecurity and the challenges involved in detecting attack behaviors, then reviews related work, including the deployment of large language models and attention mechanisms in text analysis, and highlights the limitations of current detection techniques. Our proposed method, which integrates large language models with a novel synchronized attention mechanism and synchronized loss function, covers comprehensive aspects such as the model architecture, data processing, experimental design, and evaluation metrics. This approach is theoretically innovative and has been empirically validated, proving its effectiveness and superiority in practical applications.