DOI: 10.6084/m9.figshare.30127783.v1
title: ByteEnc dataset

description: ### Introduction<br>ByteEnc dataset contains two sub-datasets as follows.<br>1. Pretraining dataset: byte sequences extracted from HWP files. - Files - data.pretrain.ngram1.ms100: Byte sequences of 1-gram - data.pretrain.ngram2.ms100: Byte sequences of 2-gram2. Malware detection dataset: byte sequences extracted from HWP, MSOffice, PDF files. - Files - data.task.HWP.test.ngram1.ms100: HWP byte sequences of 1-gram for test - data.task.HWP.test.ngram2.ms100: HWP byte sequences of 2-gram for test - data.task.HWP.train.ngram1.ms100: HWP byte sequences of 1-gram for training - data.task.HWP.train.ngram2.ms100: HWP byte sequences of 2-gram for training - data.task.MSOffice.test.ngram1.ms100: MSOffice byte sequences of 1-gram for test - data.task.MSOffice.test.ngram2.ms100: MSOffice byte sequences of 2-gram for test - data.task.MSOffice.train.ngram1.ms100: MSOffice byte sequences of 1-gram for training - data.task.MSOffice.train.ngram2.ms100: MSOffice byte sequences of 2-gram for training - data.task.PDF.test.ngram1.ms100: PDF byte sequences of 1-gram for test - data.task.PDF.test.ngram2.ms100: PDF byte sequences of 2-gram for test - data.task.PDF.train.ngram1.ms100: PDF byte sequences of 1-gram for training - data.task.PDF.train.ngram2.ms100: PDF byte sequences of 2-gram for training<br>### How to use (in python)<br>You can easily load the dataset using pickle.<br>```pythonwith open(dataset_filepath, 'rb') as f: D = pickle.load(f)```<br>##### Details of the pre-training dataset<br>When you load the pre-training dataset, then `D` is a list containing multiple dictionary objects.Every dictionary object has keys `input_ids`, `token_type_ids`, `next_sentence_label` and `metainfo`.`input_ids` and `token_type_ids` are input byte sequences and sequence labels (0 or 1) for next sentence prediction (NSP) pre-trianing algorithm.`next_sentence_label` is a binary label (0 or 1) for the NSP pre-training algorithm.`metainfo` contains malware label (0 or 1) and source file name.<br>For example, the 80010-th instance of `D` looks like below.`metainfo` contains several items, where the first and second items indicate the malware label and the source file name. Other items in `metainfo` are neglectable.<br><br>```pythonprint(D[80010])<br>{'input_ids': tensor([3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), 'next_sentence_label': tensor(0), 'metainfo': array(['1', 'stream_898783cf245a86ac2722b243a56713fe98609df3ba095f11ef82718e346f316a.csv', '\t9451520', '42', '00', '60'], dtype='```<br>If your code doesn't work due to an error related to `metainfo`, then removing it will resolve the error as follows.<br>```pythonfor d in D: d.pop('metainfo', None)```<br><br>##### Details of the malware detection dataset<br>The following is an example of `data.task.MSOffice.train.ngram1.ms100`.As you can see, the type and structure is similar to the pre-training dataset, except that the malware detection dataset has the key `labels` which is the malware binary label (0 or 1).<br><br>```pythonprint(D[0])<br>{'input_ids': tensor([3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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Ã\x81Â¾Ã\x87Ã\x95Â¼Ã\x92ÂµÃ¦Â¼Â¼Â¿Â¡ ÂµÃ»Â¸Â¥ Â³Ã³Â¾Ã®Ã\x83Ã\x8cÃ\x86Â¯ÂºÂ°Â¼Â¼ Â½Ã\x85Â°Ã\xad Ã\x87Ã¶Ã\x88Â²(Â½Ã\x85Â±Ã\x94)(p.135).xls.normal', '\t4096', 'FE', 'FF', '00'], dtype='```<br>