Cyber threats are one of the most pressing issues in the digital age. There has been a consensus on deploying a proactive defense to effectively detect and respond to adversary threats. The key to success is understanding the characteristics of malware, including their activities and manipulated resources on the target machines. The MITRE ATT&CK framework (ATT&CK), a popular source of open source intelligence (OSINT), provides rich information and knowledge about adversary lifecycles and attack behaviors. The main challenges of this study involve knowledge collection from ATT&CK, malicious behavior identification using deep learning, and the identification of associated API calls. A MITRE ATT&CK based Malicious Behavior Analysis system (MAMBA) for Windows malware is proposed, which incorporates ATT&CK knowledge and considers attentions on manipulated resources and malicious activities in the neural network model. To synchronize ATT&CK updates in a timely manner, knowledge collection can be an automatic and incremental process. Given these features, MAMBA achieves the best performance of malicious behavior discovery among all the compared learning-based methods and rule-based approaches on all datasets; it also yields a highly interpretable mapping from the discovered malicious behaviors to relevant ATT&CK techniques, as well as to the related API calls.