Dataset of dynamic malware detection based on enhanced semantic API sequence features

Dynamic malicious software detection aims to assess whether executable programs exhibit malicious behavior by thoroughly studying and analyzing their dynamic features. However, many current methodologies insufficiently explore the semantic features of API sequences and instead rely more on mining parameter information during API call processes to enhance detection performance. This leads to issues such as excessive dependence on prior knowledge, larger model parameter sizes, and higher computational complexities. To that end, this paper proposes an enhanced semantic API sequence feature dynamic malware detection scheme that integrates the RoBERTa pre-training model and gating mechanism. This scheme solely leverages API call sequences that can comprehensively capture the contextual semantic information implicitly embedded during executable file execution. Meanwhile, dynamically adjusting the weights of various modal features within the model enhances sensitivity to different malicious software samples. By fusing multimodal features, our approach comprehensively captures both the semantic and global characteristics of API sequences, enabling the model to adapt more flexibly to malware variants and thereby improving detection accuracy and robustness. Experimental results demonstrate that our proposed approach achieves classification accuracies exceeding 99% across multiple publicly available datasets.