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File fragment classification (FFC) on small chunks of memory is essential inmemory forensics and Internet security. Existing methods mainly treat filefragments as 1d byte signals and utilize the captured inter-byte features forclassification, while the bit information within bytes, i.e., intra-byteinformation, is seldom considered. This is inherently inapt for classifyingvariable-length coding files whose symbols are represented as the variablenumber of bits. Conversely, we propose Byte2Image, a novel data augmentationtechnique, to introduce the neglected intra-byte information into filefragments and re-treat them as 2d gray-scale images, which allows us to captureboth inter-byte and intra-byte correlations simultaneously through powerfulconvolutional neural networks (CNNs). Specifically, to convert file fragmentsto 2d images, we employ a sliding byte window to expose the neglectedintra-byte information and stack their n-gram features row by row. We furtherpropose a byte sequence \& image fusion network as a classifier, which canjointly model the raw 1d byte sequence and the converted 2d image to performFFC. Experiments on FFT-75 dataset validate that our proposed method canachieve notable accuracy improvements over state-of-the-art methods in nearlyall scenarios. The code will be released athttps://github.com/wenyang001/Byte2Image.

A Byte Sequence is Worth an Image: CNN for File Fragment Classification Using Bit Shift and n-Gram Embeddings