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# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2017 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
from enum import Enum
class Verbosity(int, Enum):
DEFAULT = 0
VERBOSE = 1
|
#!/usr/bin/env python3
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
# Copyright 2017 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
#!/usr/bin/env python3
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
#!/usr/bin/env python3
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
#!/usr/bin/env python3
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
#!/usr/bin/env python3
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
#!/usr/bin/env python3
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... |
# Copyright 2017 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2017 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
import pathlib
import pytest
from floss.results import StaticString, StringEncoding
from floss.language.go.extract import extract_go_strings
@pytest.fixture(scope="module")
def go_strings32():
n = 6
path = pathlib.Path(__file__).parent / "data" / "language" / "go" / "go-hello" / "bin" / "go-hello.exe"
r... |
import pathlib
import pytest
from floss.results import StaticString, StringEncoding
from floss.language.rust.extract import extract_rust_strings
@pytest.fixture(scope="module")
def rust_strings32():
n = 6
path = pathlib.Path(__file__).parent / "data" / "language" / "rust" / "rust-hello" / "bin" / "rust-hell... |
import pathlib
import contextlib
import pefile
import pytest
from floss.utils import get_static_strings
from floss.language.go.extract import extract_go_strings
from floss.language.go.coverage import get_extract_stats
@pytest.mark.parametrize(
"binary_file",
[
("data/language/go/go-unknown-binaries/... |
import pathlib
import zipfile
import pytest
from floss.language.go.extract import extract_go_strings
@pytest.fixture(scope="module")
def extract_files(request):
def _extract_files(zip_file_name, extracted_dir_name):
zip_file_path = (
pathlib.Path(__file__).parent
/ "data"
... |
from pathlib import Path
import pytest
from floss.utils import get_static_strings
from floss.language.identify import VERSION_UNKNOWN_OR_NA, Language, identify_language_and_version
@pytest.mark.parametrize(
"binary_file, expected_result, expected_version",
[
("data/language/go/go-hello/bin/go-hello.... |
import pathlib
import contextlib
import pefile
import pytest
from floss.strings import extract_ascii_unicode_strings
from floss.language.utils import get_extract_stats
from floss.language.rust.extract import extract_rust_strings
@pytest.mark.parametrize(
"binary_file",
[
(
"data/language... |
import pathlib
import zipfile
import pytest
from floss.language.rust.extract import extract_rust_strings
@pytest.fixture(scope="module")
def extract_files(request):
def _extract_files(zip_file_name, extracted_dir_name):
zip_file_path = (
pathlib.Path(__file__).parent
/ "data"
... |
import textwrap
import floss.main
# floss --no static -j tests/data/src/decode-in-place/bin/test-decode-in-place.exe
RESULTS = textwrap.dedent(
"""
{
"analysis": {
"enable_decoded_strings": true,
"enable_stack_strings": true,
"enable_static_strings": false,
"enable_tight_string... |
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2017 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... |
#!/usr/bin/env python
import os
import csv
import queue
import zipfile
import requests
import argparse
import multiprocessing
# TODO: Don't hardcode the relative path?
samples_path = "gym_malware/envs/utils/samples/"
hashes_path = "gym_malware/envs/utils/sample_hashes.csv"
vturl = "https://www.virustotal.com/vtapi/v2... |
from setuptools import setup
setup(name='gym_malware',
version='0.0.1',
install_requires=['gym','numpy','sklearn','requests','keras-rl'] # And any other dependencies the package needs
)
# note: must install https://github.com/lief-project/LIEF/releases/download/0.7.0/linux_lief-0.7.0_py3.6.tar.gz [modi... |
import numpy as np
from gym_malware.envs.utils import interface, pefeatures
from gym_malware.envs.controls import manipulate2 as manipulate
from gym_malware import sha256_train, sha256_holdout, MAXTURNS
from collections import defaultdict
from keras.models import load_model
ACTION_LOOKUP = {i: act for i, act in enum... |
import numpy as np
from gym_malware.envs.utils import interface, pefeatures
from gym_malware.envs.controls import manipulate2 as manipulate
from gym_malware import sha256_train, sha256_holdout, MAXTURNS
from collections import defaultdict
from keras.models import load_model
ACTION_LOOKUP = {i: act for i, act in enum... |
import numpy as np
import gym
import gym_malware
import chainer
import chainer.functions as F
import chainer.links as L
import chainerrl
from chainerrl.action_value import DiscreteActionValue
from chainerrl import links
from chainerrl.agents import acer
from chainerrl.distribution import SoftmaxDistribution
from ch... |
import pickle
import numpy as np
import gym
np.random.seed(123) # set a random seed when setting up the gym environment (train_test_split)
import gym_malware
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, ELU, Dropout, BatchNormalization
from keras.optimizers import Adam, SGD,... |
from gym.envs.registration import register
# get samples for environment
from gym_malware.envs.utils import interface, pefeatures
sha256 = interface.get_available_sha256()
# create a holdout set
from sklearn.model_selection import train_test_split
import numpy as np
np.random.seed(123)
sha256_train, sha256_holdout = t... |
import random
import gym
from collections import OrderedDict
from gym import error, spaces, utils
from gym.utils import seeding
import hashlib
import os
import numpy as np
from gym_malware.envs.utils import interface, pefeatures
from gym_malware.envs.controls import manipulate2 as manipulate
ACTION_LOOKUP = {i: act f... |
import random
import gym
from collections import OrderedDict
from gym import error, spaces, utils
from gym.utils import seeding
import hashlib
import os
import numpy as np
from gym_malware.envs.utils import interface, pefeatures
from gym_malware.envs.controls import manipulate2 as manipulate
ACTION_LOOKUP = {i: act f... |
from gym_malware.envs.malware_env import MalwareEnv
from gym_malware.envs.malware_score_env import MalwareScoreEnv
from gym_malware.envs import utils
|
# TODO:
# * modify exports using lief
# * zero out rich header (if it exists) --> requires updating OptionalHeader's checksum ("Rich Header" only in Microsoft-produced executables)
# * tinker with resources: https://lief.quarkslab.com/doc/tutorials/07_pe_resource.html
import lief # pip install https://github.com/lief... |
import requests
import gzip
import json
import re
import sys
import os
import glob
module_path = os.path.dirname(os.path.abspath(sys.modules[__name__].__file__))
SAMPLE_PATH = os.path.join(module_path, 'samples')
try:
# for RESTful interface to remote model
__private_data = json.load(open(os.path.join(module... |
# -*- coding: utf-8 -*-
''' Extracts some basic features from PE files. Many of the features
implemented have been used in previously published works. For more information,
check out the following resources:
* Schultz, et al., 2001: http://128.59.14.66/sites/default/files/binaryeval-ieeesp01.pdf
* Kolter and Maloof, 2... |
#######################################################################################
#
# InjectingMalwareIntoJPG.py (Injecting Malware Into JPG File) [ Main Program ]
# © 2022 ABDULKADİR GÜNGÖR All Rights Reserved
# Contact email address: abdulkadir_gungor@outlook.com
#
# Developper: Abdulkadir GÜNGÖR (abdulka... |
#######################################################################################
#
# malware_v1.py (Malware v1) [ Main Program ]
# © 2022 ABDULKADİR GÜNGÖR All Rights Reserved
# Contact email address: abdulkadir_gungor@outlook.com
#
# Developper: Abdulkadir GÜNGÖR (abdulkadir_gungor@outlook.com)
# Date: 05... |
#######################################################################################
#
# malware_v2.py (Malware v2) [ Main Program ]
# © 2022 ABDULKADİR GÜNGÖR All Rights Reserved
# Contact email address: abdulkadir_gungor@outlook.com
#
# Developper: Abdulkadir GÜNGÖR (abdulkadir_gungor@outlook.com)
# Date: 05... |
#######################################################################################
#
# malware_v3.py (Malware v3) [ Main Program ]
# © 2022 ABDULKADİR GÜNGÖR All Rights Reserved
# Contact email address: abdulkadir_gungor@outlook.com
#
# Developper: Abdulkadir GÜNGÖR (abdulkadir_gungor@outlook.com)
# Date: 05... |
import sys
import pickle
newc=pickle.load(open('cmd3g.p'))
nx={}
c=0
for i in newc:
if newc[i]>100:
c+=1
nx[i]=newc[i]
print c,len(nx)
pickle.dump(nx,open('cutcmd3g.p','w'))
|
import sys
import pickle
newc=pickle.load(open('cmd3g.p'))
nx={}
c=0
for i in newc:
if newc[i]>10000:
c+=1
nx[i]=newc[i]
print c,len(newc)
pickle.dump(nx,open('cutcmd3g_for_4g.p','w'))
|
import sys
import pickle
newc=pickle.load(open('cmd4g.p'))
nx={}
c=0
for i in newc:
if newc[i]>100:
c+=1
nx[i]=newc[i]
print c,len(newc)
pickle.dump(nx,open('cutcmd4g.p','w'))
|
import heapq
import pickle
import math
from csv import DictReader
import glob
import os
import csv
from datetime import datetime
# generate dfs features and dll call features.
#three different types: memory, constant, register
# memory: dword, word, byte
# constant: arg, var
# register: eax ebx ecx edx esi edi esp eb... |
import heapq
import pickle
import math
from csv import DictReader
import glob
import os
import csv
from datetime import datetime
# dll call features.
# load file names
def load_label(path, label):
result = []
for row in DictReader(open(path)):
if int(row['Class']) == label:
result.append(... |
import pandas as pd
s1=pd.read_csv('model1.csv',index_col=0)
s2=pd.read_csv('model2.csv',index_col=0)
s3=pd.read_csv('model3.csv',index_col=0)
for i in s1.columns.values:
s1[i]=s1[i]**0.1*s2[i]**0.4*s3[i]*0.5
s1.to_csv('ensemble.csv')
|
import pandas as pd
import sys
import numpy as np
def multiclass_log_loss(y_true, y_pred, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
https://www.kaggle.com/wiki/MultiClassLogLoss
Parameters
----------
y_true : array, shape = [n_samples]
true class, intergers in [0, n... |
import pickle
cmd=pickle.load(open('newcmd.p'))
newc={}
for c in cmd:
if '_' in c or c[0] in '?1234567890ABCDEF':
continue
else:
#print c,cmd[c]
newc[c]=cmd[c]
print newc
pickle.dump(newc,open('newc.p','w'))
|
import sys
import pickle
##########################################################
# usage
# pypy findhead.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
#############... |
import sys
import pickle
##########################################################
# usage
# pypy find_2g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
##############... |
import sys
import pickle
##########################################################
# usage
# pypy find_3g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
##############... |
import sys
import pickle
##########################################################
# usage
# pypy find_4g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
##############... |
import pickle
import os
import sys
##########################################################
# usage
# pypy find_new_ins.py xid_train.p ./ins_train ./jump_map_train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ./ins_train is where the local folder o... |
# -*- coding: utf-8 -*-
"""
part of the code borrowed from the benchmark in the forum.
create Frequency Features for 1 byte. So 16*16 features will add to train and test.
"""
from multiprocessing import Pool
import os
from csv import writer
paths = ['train','test']
def consolidate(path):
s_path = path
Fil... |
import subprocess
asm_code_path='asm_code_'
data_path='.'
cmd='mkdir '+' '.join([asm_code_path+'train',asm_code_path+'test'])
subprocess.call(cmd,shell=True)
cmd='pypy writeasm.py xid_train.p '+' '.join([data_path+'/train',asm_code_path+'train'])
subprocess.call(cmd,shell=True)
cmd='pypy writeasm.py xid_test.p '+' '.... |
import subprocess
data_path='.'
opcode_path='op_train'
jump_path='jump_train'
jump_map_path='jump_map_train'
cmd='mkdir '+' '.join([opcode_path,jump_path,jump_map_path])
subprocess.call(cmd,shell=True)
cmd='pypy get_ins.py xid_train.p '+' '.join([data_path+'/train',opcode_path])
subprocess.call(cmd,shell=True)
cmd='... |
from sklearn.ensemble import RandomForestClassifier
import pickle
import sys
import numpy as np
X1=np.array(pickle.load(open('X2g_train.p')))
X2=np.array(pickle.load(open('X3g_train.p')))
X3=np.array(pickle.load(open('X4g_train.p')))
X4=np.array(pickle.load(open('Xhead_train.p')))
X=np.hstack((X2,X1,X3,X4))
y=np.arra... |
import os
xid=[i.split('.')[0] for i in os.listdir('train') if '.asm' in i]
Xt_id=[i.split('.')[0] for i in os.listdir('test') if '.asm' in i]
f=open('trainLabels.csv')
f.readline()
label={}
for line in f:
xx=line.split(',')
idx=xx[0][1:-1]
label[idx]=int(xx[-1])
f.close()
y=[label[i] for i in xid]
import p... |
import pickle
import sys
##########################################################
# usage
# pypy getins.py xid_train.p ../../data/train ./ins_train ./jump_train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original tra... |
import pickle
import sys
xid=pickle.load(open(sys.argv[1]))
#unconditional_jump=['jmp','j','ja']
ins_path=sys.argv[2]
jump_path=sys.argv[3]
for cc,i in enumerate(xid):
jmp={}
tmp=pickle.load(open(ins_path+'/'+i+'.ins.p'))
for add in tmp:
if tmp[add] == 'jmp' or tmp[add]=='ja':
jmp[add]... |
import pickle
import sys
##########################################################
# usage
# pypy get_jump_map.py xid_train.p ../../data/train ./jump_train ./jump_map_train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of ... |
import numpy,scipy.misc, os, array
def get_feature(data_set = 'train', data_type = 'bytes'):
files=os.listdir(data_set)
with open('%s_%s_image.csv'%(data_set, data_type),'wb') as f:
f.write('Id,%s\n'%','.join(['%s_%i'%(data_type,x)for x in xrange(1000)]))
for cc,x in enumerate(files):
... |
# -*- coding: utf-8 -*-
## instructions frequency
from multiprocessing import Pool
import os
import csv
paths = ['train','test']
instr_set = set(['mov','xchg','stc','clc','cmc','std','cld','sti','cli','push',
'pushf','pusha','pop','popf','popa','cbw','cwd','cwde','in','out',
'add','adc','sub','sbb','div','idiv','m... |
import heapq
import pickle
import math
from csv import DictReader
import glob
import os
import csv
def join_ngrams(num = 100000):
dict_all = dict()
for c in range(1,10):
#print "merging %i out of 9"%c
heap = pickle.load(open('gram/ngram_%i_top%i'%(c,num),'rb'))
while heap:
c... |
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.linear_model import LogisticRegression as LGR
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.ensemble import ExtraTreesClassifier as ET
from xgboost_multi import XGBC
from sklearn import cross_validation
from sklearn.cro... |
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.linear_model import LogisticRegression as LGR
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.ensemble import ExtraTreesClassifier as ET
from xgboost_multi import XGBC
from sklearn import cross_validation
from sklearn.cro... |
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.linear_model import LogisticRegression as LGR
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.ensemble import ExtraTreesClassifier as ET
from xgboost_multi import XGBC
from sklearn import cross_validation
from sklearn.cro... |
import sys
import pickle
##########################################################
# usage
# pypy rebuild_2g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
###########... |
import os,array
import pickle
import numpy as np
import sys
xid=pickle.load(open(sys.argv[1]))
asm_code_path=sys.argv[2]
train_or_test=asm_code_path.split('_')[-1]
X = np.zeros((len(xid),2000))
for cc,i in enumerate(xid):
f=open(asm_code_path+'/'+i+'.asm')
ln = os.path.getsize(asm_code_path+'/'+i+'.asm') # len... |
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.linear_model import LogisticRegression as LGR
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.ensemble import ExtraTreesClassifier as ET
from xgboost_multi import XGBC
from sklearn import cross_validation
from sklearn.cro... |
from sklearn.ensemble import RandomForestClassifier as RF
from xgboost_multi import XGBC
from sklearn import cross_validation
from sklearn.cross_validation import StratifiedKFold as KFold
from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
import pickle
# create model_list
def get_model_list()... |
from csv import DictReader
from datetime import datetime
import pickle
import heapq
import sys
# load data
def load_label(path, label):
result = []
for row in DictReader(open(path)):
if int(row['Class']) == label:
result.append((row['Id']))
return result
# generate grams dictionary fo... |
import pickle
import sys
xid=pickle.load(open(sys.argv[1]))
data_path=sys.argv[2]
asm_code_path=sys.argv[3]
for cc,i in enumerate(xid):
f=open(data_path+'/'+i+'.asm')
fo=open(asm_code_path+'/'+i+'.asm','w')
start=True
for line in f:
xx=line.split()
for c,x in enumerate(xx):
... |
import inspect
import os
import sys
code_path = os.path.join(
os.path.split(inspect.getfile(inspect.currentframe()))[0], "xgboost-master/wrapper")
sys.path.append(code_path)
import xgboost as xgb
import numpy as np
class XGBC(object):
def __init__(self, num_round = 2, max_depth = 2, eta= 1.0, min_child_weight = 2... |
import sys
import pickle
newc=pickle.load(open('cmd3g.p'))
nx={}
c=0
for i in newc:
if newc[i]>100:
c+=1
nx[i]=newc[i]
print c,len(nx)
pickle.dump(nx,open('cutcmd3g.p','w'))
|
import sys
import pickle
newc=pickle.load(open('cmd3g.p'))
nx={}
c=0
for i in newc:
if newc[i]>10000:
c+=1
nx[i]=newc[i]
print c,len(newc)
pickle.dump(nx,open('cutcmd3g_for_4g.p','w'))
|
import sys
import pickle
newc=pickle.load(open('cmd4g.p'))
nx={}
c=0
for i in newc:
if newc[i]>100:
c+=1
nx[i]=newc[i]
print c,len(newc)
pickle.dump(nx,open('cutcmd4g.p','w'))
|
import heapq
import pickle
import math
from csv import DictReader
import glob
import os
import csv
from datetime import datetime
# generate dfs features and dll call features.
#three different types: memory, constant, register
# memory: dword, word, byte
# constant: arg, var
# register: eax ebx ecx edx esi edi esp eb... |
import heapq
import pickle
import math
from csv import DictReader
import glob
import os
import csv
from datetime import datetime
# dll call features.
# load file names
def load_label(path, label):
result = []
for row in DictReader(open(path)):
if int(row['Class']) == label:
result.append(... |
import pandas as pd
s1=pd.read_csv('model1.csv',index_col=0)
s2=pd.read_csv('model2.csv',index_col=0)
s3=pd.read_csv('model3.csv',index_col=0)
for i in s1.columns.values:
s1[i]=s1[i]**0.1*s2[i]**0.4*s3[i]*0.5
s1.to_csv('ensemble.csv')
|
import pandas as pd
import sys
import numpy as np
def multiclass_log_loss(y_true, y_pred, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
https://www.kaggle.com/wiki/MultiClassLogLoss
Parameters
----------
y_true : array, shape = [n_samples]
true class, intergers in [0, n... |
import pickle
cmd=pickle.load(open('newcmd.p'))
newc={}
for c in cmd:
if '_' in c or c[0] in '?1234567890ABCDEF':
continue
else:
#print c,cmd[c]
newc[c]=cmd[c]
print newc
pickle.dump(newc,open('newc.p','w'))
|
import sys
import pickle
##########################################################
# usage
# pypy findhead.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
#############... |
import sys
import pickle
##########################################################
# usage
# pypy find_2g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
##############... |
import sys
import pickle
##########################################################
# usage
# pypy find_3g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
##############... |
import sys
import pickle
##########################################################
# usage
# pypy find_4g.py xid_train.p ../../data/train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ../../data/train is the path of original train data
##############... |
import pickle
import os
import sys
##########################################################
# usage
# pypy find_new_ins.py xid_train.p ./ins_train ./jump_map_train
# xid_train.p is a list like ['loIP1tiwELF9YNZQjSUO',''....] to specify
# the order of samples in traing data
# ./ins_train is where the local folder o... |
# -*- coding: utf-8 -*-
"""
part of the code borrowed from the benchmark in the forum.
create Frequency Features for 1 byte. So 16*16 features will add to train and test.
"""
from multiprocessing import Pool
import os
from csv import writer
paths = ['train','test']
def consolidate(path):
s_path = path
Fil... |
import subprocess
asm_code_path='asm_code_'
data_path='.'
cmd='mkdir '+' '.join([asm_code_path+'train',asm_code_path+'test'])
subprocess.call(cmd,shell=True)
cmd='pypy writeasm.py xid_train.p '+' '.join([data_path+'/train',asm_code_path+'train'])
subprocess.call(cmd,shell=True)
cmd='pypy writeasm.py xid_test.p '+' '.... |
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