1.5 KiB
Pre-processing steps
1. run convert_space_format.py
Convert the string <space> to SPACE
linux32_0ixxxx.all -> inst.i.pos.txt located at /home/ming/malware/data/elfasm_inst_pairs
2. remove the repete lines in the inst.i.pos.txt
Using python script is too slow. We use the shell instead.
cat inst.i.pos.txt | sort -n | uniq > inst.i.pos.txt.clean
3. run create_negtive_examples.py
We use the next file of the current file as its negative examples, which is apparently rational.
Specifically, for each instruction in the current positive file, we randomly choose a line in its next file and select one of two instructions in the line as its negative example.
generate inst.i.neg.txt located at /home/ming/malware/data/elfasm_inst_pairs
4. run merge_examples_to_json.py
We dump the positive and negative examples with their corresponding labels into several json files. Each json file contains 20m lines of examples.
generate inst.i.{0-127}.json located at /home/ming/malware/inst2vec_bert/data/asm_bert
5. run check_length.py
We will specify the length padded to when we use the tokenizer, tokenizer.enable_padding(..., length=).
So we need to know the longest sentences in the dataset.