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"""Extract LIEF features from PE files"""
from h2oaicore.transformer_utils import CustomTransformer
import datatable as dt
import numpy as np
class PEExportsFeatures(CustomTransformer):
_modules_needed_by_name = ['lief==0.11.4']
_regression = True
_binary = True
_multiclass = True
_is_reproducible = True
_parallel_task = True # if enabled, params_base['n_jobs'] will be >= 1 (adaptive to system), otherwise 1
_can_use_gpu = True # if enabled, will use special job scheduler for GPUs
_can_use_multi_gpu = True # if enabled, can get access to multiple GPUs for single transformer (experimental)
_numeric_output = True
def get_default_properties():
return dict(col_type="text", min_cols=1, max_cols=1, relative_importance=1)
def do_acceptance_test():
return False
def fit_transform(self, X: dt.Frame, y: np.array = None):
return self.transform(X)
def load_pe(self, file_path):
with open(file_path, 'rb') as f:
bytez = bytearray(
return (bytez)
def exports_features(self, lief_binary):
from sklearn.feature_extraction import FeatureHasher
exports = sorted(lief_binary.exported_functions)
features_hashed = {}
if exports:
for i, x in enumerate(FeatureHasher(128, input_type='string').transform(exports).toarray()[0]):
features_hashed.update({f'Exports_functions_hash_{i}': x})
for i in range(128):
features_hashed.update({f'Exports_functions_hash_{i}': 0})
return features_hashed
def get_exports_features(self, file_path):
import lief
pe_bytez = self.load_pe(file_path)
lief_binary = lief.PE.parse(list(pe_bytez))
X = self.exports_features(lief_binary)
return X
X = {f'Exports_functions_hash_{i}': 0 for i in range(128)}
return X
def transform(self, X: dt.Frame):
import pandas as pd
ret_df = pd.DataFrame(
for x in X.to_pandas().values[:, 0]
self._output_feature_names = ret_df.columns.to_list()
self._feature_desc = self._output_feature_names
return ret_df