A malware detection system based on intermediate language

Malware remains a serious problem for economic corporations, government agencies, and individuals. With the evolution of malware, the malware detection method based on signature, reputation or anomaly show the lack of detection ability. In this paper, we proposed a novel malware detection system, which use the feature extract from intermediate representation and utilizes multiple evaluation methods to detect the malware. We extract the feature from intermediate representation with fine-grained that retain the functional sequence of malware. Then, we use LSTM (Long Sort Term Memory) to learn the pattern from the training set and build a detection system that contain many antivirus software's results to detect malware and classify it. Our approach is evaluated on more than 300,000 samples. Experimental results show that our system can effectively detect unknown malware and classify it.