A class of malware detection approaches converts benign and malicious files into control flow graphs (CFG) for improved malware identification. This helps to increase the accuracy of malware detection on the Internet of Battlefield Things (IoBTs). During the building of CFG, disassemblers are used to convert the binary code of a file into opcodes. These opcodes are then used in the creation process. chance CFGs are constructed in which the vertices represent the opcodes and the edges between the opcodes reflect the chance of occurrence of those opcodes in the file. These probability CFGs can be used to analyze and predict the behavior of programs. The probabilistic conditional fuzzy graphs (CFGs) are input into the deep learning model so that it can undergo additional training and testing. The probability of CFGs is directly proportional to the accuracy of the deep learning model. The result of the deep learning malware detection model is likely to be more accurate if the graph creation approaches can reflectorize the binary file with a higher degree of precision. In this study, we highlight the limitations of the existing probability CFG techniques, suggest a new strategy for the generation of probability CFGs that is a combination of crisp and heuristic approaches and calls itself HeuCrip, and then compare the proposed technique with the existing state-of-the-art schemes. The findings of the experiments indicate that the HeuCrip obtained an accuracy of 99.93% and demonstrates a considerable improvement in performance in comparison to other state-of-the-art methods currently in use