def get_ow_train_test(path, outpath_train, outpath_test):
"""
extract opened world instances, random take 20,000 training set
the rest of ow instances as ow testing data
output csv file respectively
:return:
"""
"get ow instnace"
x, y = utils_wf.get_ow_data(path)
x = pd.DataFrame(x)
candit_list = list(range(0, len(y)))
rand_list = random.sample(candit_list, 20000)
X_train, X_test, y_train, y_test = [], [], [], []
for i in range(len(y)):
if i in rand_list:
X_train.append(x.iloc[i, :])
y_train.append(y[i])
else:
X_test.append(x.iloc[i, :])
y_test.append(y[i])
train_ow = utils_wf.convert2dataframe(X_train, y_train)
test_ow = utils_wf.convert2dataframe(X_test, y_test)
utils_wf.write2csv(train_ow, outpath_train)
utils_wf.write2csv(test_ow, outpath_test)
def main2csv():
"convert burst file to csv file"
data_type = ['test', 'train']
for type in data_type:
path = '../data/WalkieTalkie/defended_batch/%s/' % type
out_path = '../data/WalkieTalkie/defended_csv/adv_%s_WT.csv' % type
items = os.listdir(path)
data_list = []
labels = []
for item in items:
if item[-6:] == '.burst':
label = int(item.split('-')[0])
data = load_burst_file(path + item)
data_list.append(data)
labels.append(label)
# tt = pd.DataFrame(data_list)
# tt['label'] = labels
# tt.to_csv('../data/WalkieTalkie/defended_csv/orig_%s.csv' % type,index=0)
"binary to burst"
data_burst, data_burst_noSlice = utils_wf.burst_transform(
data_list, slice_threshold=512)
data_df = utils_wf.convert2dataframe(data_burst_noSlice,
labels,
mode='padding')
utils_wf.write2csv(data_df, out_path)
print('{} ... saved successfully.'.format(out_path))
def merge_data(slice_threshold):
"merge train/test/valid data into one csv file processed source data (remove less than 50 packets and starting with incoming packet)"
"output data in burst format"
data_folder = '../data/wf_ow/'
out_folder = data_folder + '/input_size_' + str(slice_threshold) + '/'
if not os.path.exists(out_folder):
os.mkdir(out_folder)
X_path = [
data_folder + 'X_train_NoDef.pkl',
data_folder + 'X_test_Unmon_NoDef.pkl',
data_folder + 'X_test_Mon_NoDef.pkl', data_folder + 'X_valid_NoDef.pkl'
]
Y_path = [
data_folder + 'y_train_NoDef.pkl',
data_folder + 'y_test_Unmon_NoDef.pkl',
data_folder + 'y_test_Mon_NoDef.pkl', data_folder + 'y_valid_NoDef.pkl'
]
out_path = out_folder + 'data_NoDef_processed.csv'
X, Y = [], []
for x_path, y_path in zip(X_path, Y_path):
X += utils_wf.load_pkl_data(x_path)
Y += utils_wf.load_pkl_data(y_path)
print('data instances after merged: {}'.format(len(Y)))
"remove less than 50 packets and starting with incoming packet"
X_new, Y_new = utils_wf.data_preprocess(X, Y)
print('data instances after processed: {}'.format(len(Y_new)))
"convert to burst"
x_burst, _ = utils_wf.burst_transform(X_new, slice_threshold)
data_new = utils_wf.convert2dataframe(x_burst, Y_new)
utils_wf.write2csv(data_new, out_path)
def tranform2burst(path, out_path, slice_threshold):
"load processed source data from csv, transform to burst with certain fixed size and write it in csv"
x, y = utils_wf.load_csv_data(path)
x_burst, x_burst_nopadding = utils_wf.burst_transform(x, slice_threshold)
burst_data = utils_wf.convert2dataframe(x_burst, y, mode='padding')
utils_wf.write2csv(burst_data, out_path)
def extract_balance_data(path, num, outpath, data_type):
"extract num instances for each class, write into csv"
if data_type == 'cw': #[ow,cw]
X, Y = utils_wf.extract_data_each_class(path, num)
elif data_type == 'ow':
X, Y = utils_wf.extract_data_each_class_ow(path, num)
data = utils_wf.convert2dataframe(X, Y, mode='NoPadding')
utils_wf.write2csv(data, outpath)
def build_ow_train(path1, path2, outpath):
"""
concat two dataset
:param path1: ow train data
:param path2: cw train data
:return:
"""
X_1, y_1 = utils_wf.load_csv_data(path1)
X_2, y_2 = utils_wf.load_csv_data(path2)
train_1 = utils_wf.convert2dataframe(X_1, y_1)
train_2 = utils_wf.convert2dataframe(X_2, y_2)
merge = [train_1, train_2]
train_merge = pd.concat(merge)
utils_wf.write2csv(train_merge, outpath)
def gen_split_file(split_id, X, y, Adversary):
"""
:param split_id: index for splitting
:param X: X data need to be splitted
:param y: y label to split
:return:
"""
"save splitted file in csv"
for i, id in enumerate(split_id):
print('*' * 30)
print('split %d' % i)
train_id = id[0]
test_id = id[1]
print('train_id', len(train_id))
print('test id', len(test_id))
# for train_id, test_id in id:
df_train = utils_wf.convert2dataframe(X.iloc[train_id],
y.iloc[train_id])
df_test = utils_wf.convert2dataframe(X.iloc[test_id], y.iloc[test_id])
utils_wf.write2csv(df_train, get_output_name('train', i, Adversary))
utils_wf.write2csv(df_test, get_output_name('test', i, Adversary))
def pkl2burst_csv(slice_threshold):
"""
load pkl file, transform data to burst and write it into csv
No preprocessing: keep the original way. do not remove instance less than 50 packets and starting with incoming packet
"""
type = 'valid' #['test', 'train', 'valid']
folder_name = 'wf_ow' #[
data_name = 'Nodef' #['NoDef', 'WalkieTalkie' ]
# x_path = '../data/' + folder_name + '/X_' + type + '_' + data_name + '.pkl'
# out_path = '../data/' + folder_name + '/' + type + '_' + data_name + '.csv'
# y_path = '../data/' + folder_name + '/y_' + type + '_' + data_name + '.pkl'
# x_path = '../data/NoDef/X_valid_NoDef.pkl'
# out_path = '../data/NoDef/valid_NoDef.csv'
# y_path = '../data/NoDef/y_valid_NoDef.pkl'
# x_path = '../data/NoDef/X_train_NoDef.pkl'
# out_path = '../data/NoDef/train_NoDef.csv'
# y_path = '../data/NoDef/y_train_NoDef.pkl'
"processing pkl file"
# x = utils_wf.load_pkl_data(x_path)
# y = utils_wf.load_pkl_data(y_path)
"processing wang data csv file"
file_id = 'wang_UnMon'
x_path = '../data/wf_wang/' + file_id + '.csv'
out_path = '../data/wf_wang/' + file_id + '_burst.csv'
x, y = utils_wf.load_csv_data(x_path)
# X_new, y_new = utils_wf.data_preprocess(x, y) #remove trace less than 50 packets and starting with incoming packet
x_burst, x_burst_noSlicing = utils_wf.burst_transform(x, slice_threshold)
# utils_wf.size_distribution(x_burst_noSlicing,file_id)
burst_data = utils_wf.convert2dataframe(x_burst, y)
utils_wf.write2csv(burst_data, out_path)
def write2csv(self, X, Y):
df = utils_wf.convert2dataframe(X, Y, mode='NoPadding')
df.to_csv(self.data_path + '/' + self.opts['output_file'], index=0)
print('file has been processed successfully!')
