Malware has always been a big problem for companies, government agencies, and individuals because people still use it as a primary tool to influence networks, applications, and computer operating systems to gain unilateral benefits. Until now, malware detection with heuristic and signature-based methods are still struggling to keep up with the evolution of malware. Machine learning is known to be able to automate the work needed to detect families of existing and newly discovered malware. Unfortunately, the machine learning method using Support Vector Machine (SVM) for detecting malware can only reach a low level of accuracy. In this work, we propose a dynamic analysis method and uses a system call sequence to monitor malware behavior. It uses the word2vec technique as word embedding and implements deep learning models, namely Long Short-Term Memory (LSTM) and Nested LSTM, as classifiers. To compare with existing machine learning approach, we also apply the Support Vector Machine (SVM) as a benchmark method. The Nested LSTM gets an accuracy of 93.11%, while the LSTM gets the best accuracy of 98.61%. The LSTM also achieved the best performance in terms of average precision at 97.57%, the average recall at 97.29%, and the average score of f1 at 97.43%. We have found that our model is lightweight but powerful for detecting malware with significant accuracy.