Emerging complex multi-step attacks such as Advanced Persistent Threats (APTs) pose significant risks to national economic development, security, and social stability. Effectively detecting these sophisticated threats is a critical challenge. While deep learning methods show promise in identifying unknown malicious behaviors, they often struggle with fragmented modal information, limited feature representation, and generalization. To address these limitations, we propose GATransformer, a new dual-modal detection method that integrates topological structure analysis with temporal sequence modeling. Its core lies in a cross-attention semantic fusion mechanism, which deeply integrates heterogeneous features and effectively mitigates the constraints of unimodal representations. GATransformer reconstructs network behavior representation via a parallel processing framework in which graph attention captures intricate spatial dependencies, and self-attention focuses on modeling long-range temporal correlations. Experimental results on the CIDDS-001 and CIDDS-002 datasets demonstrate the superior performance of our method compared to baseline methods with detection accuracies of 99.74% (nodes) and 88.28% (edges) on CIDDS-001 and 99.99% and 99.98% on CIDDS-002, respectively.
Zhu et al. (Thu,) studied this question.