def tianchi_api():
api_vec = TfidfVectorizer(ngram_range=(1, 5),
min_df=3,
max_df=0.9,
strip_accents='unicode',
use_idf=1,
smooth_idf=1,
sublinear_tf=1,
max_features=500)
white = pd.read_csv(os.path.join("features",
"white.csv"))[['file_name', 'api_name']]
black = load_df(os.path.join("features", "black"))
test = load_df(os.path.join("features", "test"))
tianchi = pd.read_csv("security_train.csv").rename(
columns={"file_id": "file_name"})
full = pd.concat([white, black, test])
full_str = to_str(full)
print(1)
api_vec.fit(full_str)
print(2)
black_output, name_list = to_str(tianchi, mode=1, column_name="api")
save_dict(name_list, os.path.join("features", "tianchi_name_list"))
black_output = api_vec.transform(black_output)
scipy.sparse.save_npz(os.path.join("features", "tianchi.npz"),
black_output)
def stage2_api(feature_num=500):
api_vec = TfidfVectorizer(ngram_range=(1, 5),
min_df=3,
max_df=0.9,
strip_accents='unicode',
use_idf=1,
smooth_idf=1,
sublinear_tf=1,
max_features=feature_num)
white = pd.read_csv(os.path.join("features",
"white.csv"))[['file_name', 'api_name']]
black = load_df(os.path.join("features", "black"))
test = load_df(os.path.join("features", "test"))
stage2 = load_df(os.path.join("features", "stage2"))
full = pd.concat([white, black, test])
full_str = to_str(full)
print(1)
api_vec.fit(full_str)
print(2)
black_output, name_list = to_str(stage2, mode=1)
save_dict(name_list,
os.path.join("features", "stage2_name_list" + str(feature_num)))
black_output = api_vec.transform(black_output)
scipy.sparse.save_npz(
os.path.join("features", "stage2" + str(feature_num) + ".npz"),
black_output)
def api():
api_vec = TfidfVectorizer(ngram_range=(1, 5),
min_df=3,
max_df=0.9,
strip_accents='unicode',
use_idf=1,
smooth_idf=1,
sublinear_tf=1,
max_features=500)
white = pd.read_csv("white.csv")[['file_name', 'api_name']]
black = load_df("black")
test = load_df("test")
full = pd.concat([white, black, test])
full_str = to_str(full)
print(1)
api_vec.fit(full_str)
print(2)
black_output, name_list = to_str(black, mode=1)
save_dict(name_list, "black_name_list")
black_output = api_vec.transform(black_output)
scipy.sparse.save_npz("black.npz", black_output)
white_output, name_list = to_str(white, mode=1)
save_dict(name_list, "white_name_list")
white_output = api_vec.transform(white_output)
scipy.sparse.save_npz("white.npz", white_output)
test_str, name_list = to_str(test, mode=1)
save_dict(name_list, "test_name_list")
test_output = api_vec.transform(test_str)
scipy.sparse.save_npz("test.npz", test_output)
def stage2_api_new(feature_num=500):
api_vec = TfidfVectorizer(ngram_range=(1, 5),
min_df=3,
max_df=0.9,
strip_accents='unicode',
use_idf=1,
smooth_idf=1,
sublinear_tf=1,
max_features=feature_num)
stage2 = load_df(os.path.join("features", "stage2"))
black_output, name_list = to_str(stage2, mode=1)
print(1)
api_vec.fit(black_output)
print(2)
# black_output, name_list = to_str(stage2, mode=1)
save_dict(
name_list,
os.path.join(get_root_path(), "features",
"stage2_name_list" + str(feature_num)))
black_output = api_vec.transform(black_output)
scipy.sparse.save_npz(
os.path.join(get_root_path(), "features",
"stage2" + str(feature_num) + ".npz"), black_output)
def stage_2_attribute(suffix="_dll",
use_less_value=False,
type_name="",
map_func=None,
max_feature=1000):
stage2 = load_df(os.path.join("features", "stage2" + suffix), mode=1)
if use_less_value:
if map_func is None:
stage2["value"] = stage2["value"].map(lambda x: x.split("\\")[-1])
else:
stage2["value"] = stage2["value"].map(lambda x: map_func(x))
stage2_output, name_list = to_str(stage2, mode=1, column_name="value")
api_vec, _ = train_tf_idf(suffix="_dll",
use_less_value=use_less_value,
map_func=map_func,
data=stage2_output,
max_feature=max_feature)
save_dict(
name_list,
os.path.join(get_root_path(), "features",
"stage2_name_list" + suffix + type_name))
stage2_output = api_vec.transform(stage2_output)
scipy.sparse.save_npz(
os.path.join(get_root_path(), "features",
"stage2" + suffix + type_name + ".npz"), stage2_output)
def train_tf_idf(suffix="_dll",
use_less_value=False,
map_func=None,
max_feature=2000,
data=None):
api_vec = TfidfVectorizer(ngram_range=(1, 5),
min_df=3,
max_df=0.9,
strip_accents='unicode',
use_idf=1,
smooth_idf=1,
sublinear_tf=1,
max_features=max_feature)
if data is None:
white = load_df(os.path.join(get_root_path(), "features",
"white" + suffix),
mode=1)
black = load_df(os.path.join(get_root_path(), "features",
"black" + suffix),
mode=1)
test = load_df(os.path.join(get_root_path(), "features",
"test" + suffix),
mode=1)
if use_less_value:
if map_func is None:
for i in [white, black, test]:
i["value"] = i["value"].map(lambda x: x.split("\\")[-1])
else:
for i in [white, black, test]:
i["value"] = i["value"].map(lambda x: map_func(x))
full = pd.concat([white, black, test])
full_str = to_str(full, column_name="value")
else:
full_str = data
print(1)
api_vec.fit(full_str)
print(2)
if data is None:
return api_vec, [white, black, test]
else:
return api_vec, None
def inter(load_name):
white = load_df(load_name, mode=1)[[
'file_name', 'api_name', 'ret_value', 'call_time'
]]
white.rename(columns={
'ret_value': 'return_value',
'api_name': 'api',
'file_name': 'file_id',
'call_time': 'index'
},
inplace=True)
return white
def extract_features(load_name, extract_function):
white = load_df(load_name, mode=1)[[
'file_name', 'api_name', 'call_name', 'call_pid', 'ret_value',
'apiArg_list_count', 'exInfo_list_count', 'call_time'
]]
white.rename(columns={
'ret_value': 'return_value',
'api_name': 'api',
'file_name': 'file_id',
'call_time': 'index'
},
inplace=True)
a = extract_function(white)
a.rename(columns={'file_id': 'file_name'}, inplace=True)
return a
from sklearn.model_selection import train_test_split
from basic_function import load_dict, load_df, get_root_path, save_dict
from metrics import com_acc
from model import get_model
from shorten_api_list import delete_repeat_pattern, delete_same_pattern
import pickle
shape = (512, 64)
input_dim = 92 + 1
batch_size = 32
epochs = 50
class_num = 2
api_dict = load_dict(os.path.join(get_root_path(), "features", "api_dict.txt"))
white = load_df(os.path.join(get_root_path(), "features", "white"),
mode=1)[['file_name', 'api_name', 'call_time']]
black = load_df(os.path.join(get_root_path(), "features", "black"),
mode=1)[['file_name', 'api_name', 'call_time']]
white_label = np.zeros(white.shape[0])
black_label = np.ones(black.shape[0])
full = pd.concat([white, black], sort=False)
label = np.concatenate((white_label, black_label))
full['label'] = label
full['api_name'] = full['api_name'].map(api_dict)
# full = load_df(os.path.join(get_root_path(), "features", "stage2"), mode=1)[['file_name', 'api_name', 'call_time']]
full['label'] = np.zeros((full.shape[0], ))
full['api_name'] = full['api_name'].map(api_dict)
from basic_function import load_df, save_dict
import pandas as pd
full = load_df("../features/stage2")
# full.fillna("None", inplace)
api_list = set(full['api_name'])
print(len(api_list))
api_dict = dict(zip(api_list, range(len(api_list))))
save_dict(api_dict, "../features/api_dict.txt")
