Malware behavioral indicators denote those potentially high-risk malicious behaviors exhibited, such as unintended network communications, file encryption, keystroke logging, sandbox evasion, and camera manipulation. Generally, they are generated using sandboxes or simulators. However, the complexity of modern malware has been considerably increased. Malware is becoming sandbox-aware by incorporating modern evasive techniques. To address these issues, I propose a new neural network-based static scanner that can characterize the malicious behaviors of a given executable, without running it in a sandbox. It can be used as an additional binary analytic layer to mitigate the issues of polymorphism, metamorphism, and evasive techniques.