Intelligent Network Security Detection Model: Application of Multi-Scale Attention Network and Distributed Perception Mechanism

To address the complex and variable attack patterns prevalent in the current cybersecurity landscape, as well as the limitations of traditional detection methods concerning sensing range, accuracy and efficiency, an innovative intelligent detection model for cybersecurity was proposed. First, we design a distributed multi-target finite sensing mechanism with complementary fields of view, significantly extending the sensing range by optimizing sensor layout and collaboration strategies. Second, this study constructs a multi-scale attention network model (MSANet), which enhances the feature extraction and expression capabilities of the model without imposing additional computational burdens, thereby enabling more accurate recognition of cyber-attack patterns across different scales. Finally, leveraging the extensive data provided by the distributed perception system and the robust learning capabilities of MSANet, we develop a label-free intelligent detection model for network security. This model effectively addresses the detection challenges arising from feature distribution discrepancies between the target and source network domains, achieving efficient and accurate detection in environments with no labeled or limited labeled data. This advancement provides substantial technical support for network security protection. Experimental results demonstrate that our approach achieves accuracy rates of 98.2%, 96.7% and 99.5%, as well as F1-scores of 97.9%, 95.8% and 97.9%, respectively, in detecting botnet traffic, background traffic and normal traffic within the CTU network traffic dataset. In summary, this study not only enriches the theoretical framework of network security detection but also offers practical solutions for constructing an efficient and intelligent network security protection system, possessing significant theoretical value and promising application prospects.