def embedd_configuration(db_name, model, batch_size, max_instruction,
embeddings_table):
msg = "Embedding options: \n"
msg += " - Database Name: {} \n".format(db_name)
msg += " - Model: {} \n".format(model)
msg += " - Batch Size: {} \n".format(batch_size)
msg += " - Max Instruction per function: {} \n".format(max_instruction)
msg += " - Table for saving embeddings: {}.".format(embeddings_table)
return msg
if __name__ == '__main__':
print_safe()
parser = argparse.ArgumentParser(description=debug_msg)
parser.add_argument("-db",
"--db",
help="Name of the database to create",
required=True)
parser.add_argument("-b",
"--build",
help="Build db disassebling executables",
action="store_true")
parser.add_argument("-s",
"--split",
help="Perform data splitting for training",
for function in functions:
if functions[function]['address'] == address:
instructions_list = functions[function]['filtered_instructions']
break
if instructions_list is None:
print("Function not found")
return None
converted_instructions = self.converter.convert_to_ids(instructions_list)
instructions, length = self.normalizer.normalize_functions([converted_instructions])
embedding = self.embedder.embedd(instructions, length)
return embedding
if __name__ == '__main__':
utils.print_safe()
parser = ArgumentParser(description="Safe Embedder")
parser.add_argument("-m", "--model", help="Safe trained model to generate function embeddings")
parser.add_argument("-i", "--input", help="Input executable that contains the function to embedd")
parser.add_argument("-a", "--address", help="Hexadecimal address of the function to embedd")
args = parser.parse_args()
address = int(args.address, 16)
safe = SAFE(args.model)
embedding = safe.embedd_function(args.input, address)
print(embedding[0])