LM-cAPI:A Lite Model Based on API Core Semantic Information for Malware Classification

Currently, malware is continually evolving and growing in complexity, posing a significant threat to network security. With the constant emergence of new types and quantities of malware coupled with the continuous updating of dissemination methods, the rapid and accurate identification of malware as well as providing precise support for corresponding warning and defense measures have become a crucial challenge in maintaining network security. This article focuses on API call sequences in malware that can characterize the behavioral characteristics of malware as text and then uses the latest text classification-related technologies to achieve the classification of malware. This article proposes a flexible and lightweight malicious code classification model based on API core semantic information. To address the issues of prolonged training time and low accuracy caused by excessive noise and redundant data in API call sequences, this model adopts an intimacy analysis method based on a self-attention mechanism for key information extraction. To enhance the capture of semantic information within malware API call sequences, a feature extraction model based on a self-attention mechanism is used to transform unstructured key API sequences into vector representations, extract core features, and finally connect to the TextCNN model for multi classification. In the dataset of the “Alibaba Cloud Security Malicious Program Detection” competition, the F1 value reached 90% in eight category classification tasks. The experimental results show that the model proposed in this article can achieve better results in malware detection and multi-classification.