Abstract:

[Application Background] In the process of wireless network cloud data application, the integrated detection path cloud data sequence is relatively single based on the attention of a single neighborhood information, and the output value of the detection matrix has singularity. [Scope of Literature] Under multi-step forgery attacks, existing methods may cause abnormal fluctuations in the MSE, MAE, and GAME values [Purpose] To meet the requirements of adaptive detection, this paper proposes an adaptive detection method for wireless network cloud data forgery attacks based on a Transformer model. [Method] The proposed method analyzes the attention to multi-hop reverse neighborhood information in suspicious paths and integrates neighborhood information from multiple nodes to construct a multi-step forgery attack detection path for wireless network cloud data. Based on the Transformer model, the input sequence of the detection path is mapped into multiple subspaces, where independent attention mechanisms are applied in each subspace. A detection matrix for the path input data sequence is then established, and an adaptive language learning dictionary for the cloud data attack detection matrix is generated, thereby enabling accurate detection of wireless network cloud data forgery attacks. [Result] Experimental results show that, in the aggregated detection path, the maximum MAE value of the method proposed in reference [3] is higher than 0.009, the MSE value reaches above 0.09, and the GAME value reaches above 0.07, indicating a large detection error. However, the MSE value of the proposed method only changes within 0.002, the MAE value changes within 0.02, and the GAME value changes within 0.01, indicating smaller adaptive detection errors and higher detection accuracy for forgery attacks. [Limitations] This method requires high-performance hardware support and cross platform compatibility. [Conclusion] The adaptive detection accuracy of forgery attacks is high, which plays an important role in improving the security of wireless network data.

Key words: Transformer language model, wireless network, cloud data, fake attacks, adaptive detection method