A Malware-Detection Method Using Deep Learning to Fully Extract API Sequence Features

Due to the rapid emergence of malware and its greater harm, the successful execution of malware often brings incalculable losses. Consequently, the detection of malware has become increasingly crucial. The sequence of API calls in software embodies substantial behavioral information, offering significant advantages in the identification of malicious activities. Meanwhile, the capability of automatic feature extraction by deep learning can better mine the features of API call sequences. In the current research, API features remain underutilized, resulting in suboptimal accuracy in API detection. In this paper, we propose a deep-learning-based method for detecting malware using API call sequences. This method transforms the API call sequence into a grayscale image and performs classification in conjunction with sequence features. By leveraging a range of deep-learning algorithms, we extract diverse behavioral information from software, encompassing semantic details, time-series information, API call frequency data, and more. Additionally, we introduce a specialized neural network framework and assess the impact of pixel size on classification effectiveness during the grayscale image-mapping process. The experimental results show that the accuracy of our classification method is as high as 99%. Compared with other malware-detection techniques, especially those based on API call sequences, our method maps API call sequences to gray image analysis and has higher detection accuracy.