Abstract: Malware ID expects a critical part in network security with the expansion in malware improvement. What more, kinds of progress in cutting edge assaults. Noxious programming applications, or malware, are the principal wellspring of different security issues. For different reasons, including the taking of state of the art developments and insightful properties, regulative exhibitions of retaliation, and the modification of sensitive information, to give some examples, these pernicious applications plan to perform unapproved exercises on the host machines to assist their makers. More valuable assistance systems are required because of the quick expansion of noxious programming on the web and their self changing skills, as in polymorphic and remarkable malware. This task proposes to support the MalFree Sandbox with stacked bidirectional long transient memory (Stacked BiLSTM) and generative prepared transformer based (GPT2) critical learning language models for recognizing pernicious code isolated. The proposed computations, specifically the bidirectional long transient memory (BiLSTM) model and the generative prepared transformer 2 (GPT-2) method, employ gathering rules derived from Minimal Executable (PE) Records static examination results to identify harmful code pieces. To comprehend malwares through MalFree Sandbox, care should be taken to sandbox the malwares in a climate that considers an encapsulation and exhaustive evaluation while in addition keeping on propelling spread from being gifted.