An API Semantics-Aware Malware Detection Method Based on Deep Learning

Abstract

The explosive growth of malware variants poses a continuously and deeply evolving challenge to information security. Traditional malware detection methods require a lot of manpower. However, machine learning has played an important role on malware classification and detection, and it is easily spoofed by malware disguising to be benign software by employing self-protection techniques, which leads to poor performance for existing techniques based on the machine learning method. In this paper, we analyze the local maliciousness about malware and implement an anti-interference detection framework based on API fragments, which uses the LSTM model to classify API fragments and employs ensemble learning to determine the final result of the entire API sequence. We present our experimental results on Ali-Tianchi contest API databases. By comparing with the experiments of some common methods, it is proved that our method based on local maliciousness has better performance, which is a higher accuracy rate of 0.9734.