A system and method for detecting embedded malware from a device including a receiver for receiving embedded binary image; a memory for encoding and storing the received embedded binary image; and one or more processors coupled to the receiver. The method includes extracting statistical features from the encoded embedded binary image; producing gridded data from the statistical features, using SV; inputting the gridded data to a machine learning (ML) trained to detect embedded malware from the gridded data; and determining whether the embedded binary image is benign or malware.