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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 PENormalizedByteCount(CustomTransformer):
_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 __init__(self, **kwargs):
super().__init__(**kwargs)
@staticmethod
def get_default_properties():
return dict(col_type="text", min_cols=1, max_cols=1, relative_importance=1)
@staticmethod
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(f.read())
return(bytez)
def get_norm_byte_count(self, file_path):
try:
pe_bytez = self.load_pe(file_path)
pe_int = np.frombuffer(pe_bytez, dtype=np.uint8)
# Calculate normalized byte counts
counts = np.bincount(pe_int, minlength=256)
X = counts / counts.sum()
return X
except:
X = np.zeros(256, dtype=np.float32)
return X
def transform(self, X: dt.Frame):
import pandas as pd
orig_col_name = X.names[0]
ret_df = pd.DataFrame(
[
self.get_norm_byte_count(x)
for x in X.to_pandas().values[:,0]
]
)
self._output_feature_names = ['ByteNormCount_{}'.format(x) for x in range(ret_df.shape[1])]
self._feature_desc = [f'Normalized Count of Byte value {x} for {orig_col_name} column' for x in range(ret_df.shape[1])]
return ret_df
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