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CleanMFT.py
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CleanMFT.py
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"""
@Author: J. Alexander
@Date: 7/9/2017
@Version: 1.0
Program Purpose: This is a variation of the other MFTCleaner.py. I'm writing this program
to learn how to write APIs.
This program should probably be split up so that it sends the CSV to the server before
filtering it. The only problem is that the CSV is too big to send as a whole over HTTP
without breaking it up first.
Maybe we can use the client machine to do the initial filtering and then have the server
program run a variation of sentiment analysis on the filtered program.
EXAMPLE USAGE: ~$ python CleanMFT.py /User/glassCodeBender/Documents/mft_csv.csv username
"""""
import pandas as pd
import re
import sys
import os
from requests import put, get
import datetime
import array
import socket
class CleanMFT:
def __init__(self, import_file = sys.argv(1), id = "port_number", reg_file=True, output_filename = os.getcwd() + "result.csv",
suspicious=False, start_date='', end_date='', start_time='', end_time='', filter_index = ''):
if os.path.isfile(import_file):
self.__file = import_file # stores the FQDN of the MFT CSV file
self.__id = id # this value will be used as the id at the end of the URL.
self.__reg_file = reg_file # accepts a txt file
self.__suspicious = suspicious
self.__start_date = start_date # accepts a date to filter
self.__end_date = end_date
self.__start_time = start_time # accepts a time to filter
self.__end_time = end_time
self.__output_file = output_filename
self.__filter_index = filter_index
""" This is the main method of the program. """
def run(self):
sdate, edate, stime, etime = self.__start_date, self.__end_date, self.__start_time, self.__end_time
output_file = self.__output_file
suspicious = self.__suspicious
mft_csv = self.__file
reg_file = self.__reg_file
id = self.__id
sindex, eindex = [x.strip() for x in self.__filter_index.split(',')]
if sindex.contains(',') or eindex.contains(','):
sindex.replace(',', '')
eindex.replace(',', '')
if not sindex.isdigit and eindex.isdigit:
raise ValueError("ERROR: The index value you entered to filter the table by was improperly formatted. \n"
"Please try to run the program again with different values.")
df = pd.DataFrame()
df = df.from_csv(mft_csv, sep='|', parse_dates=[[0, 1]])
# df = df.from_csv("MftDump_2015-10-29_01-27-48.csv", sep='|')
# df_attack_date = df[df.index == '2013-12-03'] # Creates an extra df for the sake of reference
df = df.reset_index(level=0, inplace=True)
if sindex and eindex:
df = df[sindex : eindex]
if reg_file:
df = self.filter_by_filename(df)
if suspicious:
df = self.filter_suspicious(df)
if sdate or edate or stime or etime:
df = self.filter_by_dates(df)
filtered_df = df.to_csv(index=True) # To make this easier, we'll send the CSV over socket as an Array
# create an array of lines from the DataFrame CSV
for line in filtered_df:
arr = array.append(line)
# server address we're sending get request to.
address = 'http://localhost/' + id
port_number = get(address)
# send the array via the socket
send_from(arr, (address + + ":" + port_number('task')))
"""
Send an array over a socket.
"""
def send_from(arr, dest):
view = memoryview(arr).cast('B')
while len(view):
nsent = dest.send(view)
view = view[nsent:]
"""
Receive an array over a socket.
"""
def recv_into(arr, source):
view = memoryview(arr).cast('B')
while len(view):
nrecv = source.recv_into(view)
view = view[nrecv:]
"""
Read a file line by line and return a list with items in each line.
@Param A Filename
@Return A list
"""
def read_file(self, file):
list = []
with open(file) as f:
for line in f:
list.append(line)
return list
"""
Method to filter a list of words and concatenate them into a regex
@Param List of words provided by user to alternative file.
@Return String that will be concatenated to a regex.
"""
def update_reg(self, list):
s = '|'
new_reg = s.join(list)
return new_reg
"""
Filters a MFT csv file that was converted into a DataFrame to only include relevant extensions.
@Param: DataFrame
@Return: DataFrame - Filtered to only include relevant file extensions.
"""
def filter_by_filename(self, df):
reg_file = self.__reg_file
reg_list = self.read_file(reg_file)
user_reg = self.update_reg(reg_list)
if user_reg is not None:
pattern = r'' + user_reg
else:
pattern = r'.exe|.dll|.rar|.sys|.jar'
regex1 = re.compile(pattern, flags=re.IGNORECASE)
df['mask'] = df[['Filename', 'Desc']].apply(lambda x: x.str.contains(regex1, regex=True)).any(axis=1)
filt_df = df[df['mask'] == True]
pattern2 = r'Create$|Entry$'
regex2 = re.compile(pattern2, flags=re.IGNORECASE)
filt_df['mask2'] = filt_df[['Type']].apply(lambda x: x.str.contains(regex2, regex=True)).any(axis=1)
filtered_df = filt_df[filt_df['mask2'] == True]
filtered_df.drop(['mask', 'mask2'], axis=1, inplace=True)
return filtered_df
"""
Filters a MFT so that only the executables that were run outside Program Files are
included in the table.
@Param: DataFrame
@Return: DataFrame - Filtered to only include relevant file extensions.
"""
def filter_suspicious(self, df):
pattern = r'^.+(Program\sFiles|System32).+[.exe]$'
regex1 = re.compile(pattern)
df['mask'] = df[['Filename', 'Desc']].apply(lambda x: x.str.contains(regex1, regex=True)).any(axis=1)
filt_df = df[df['mask'] == False]
pattern2 = r'.exe$'
regex2 = re.compile(pattern2)
filt_df['mask2'] = filt_df[['Filename', 'Desc']].apply(lambda x: x.str.contains(regex2, regex=True)).any(axis=1)
filtered_df = filt_df[filt_df['mask2'] == True]
filtered_df.drop(['mask', 'mask2'], axis=1, inplace=True)
return filtered_df
"""
Filters a MFT csv file that was converted into a Dataframe to only include the
occurrences of certain dates and/or times.
@Param: DataFrame
@Return: DataFrame - Filtered to only include relevant virus names.
"""
def filter_by_dates(self, df):
sdate = self.__start_date
edate = self.__end_date
stime = self.__start_time
etime = self.__end_time
if edate and sdate and etime and stime:
s_stamp = pd.Timestamp(sdate + ' ' + stime)
e_stamp = pd.Timestamp(edate + ' ' + etime)
filtered_df = df[s_stamp:e_stamp]
elif sdate and edate and etime and not stime:
s_stamp = pd.Timestamp(sdate)
e_stamp = pd.Timestamp(edate + ' ' + etime)
filtered_df = df[s_stamp:e_stamp]
elif sdate and edate and stime:
s_stamp = pd.Timestamp(sdate + ' ' + stime)
e_stamp = pd.Timestamp(edate)
filtered_df = df[s_stamp:e_stamp]
elif sdate and stime:
s_stamp = pd.Timestamp(sdate + ' ' + stime)
filtered_df = df[s_stamp:]
elif edate and etime:
e_stamp = pd.Timestamp(edate + ' ' + etime)
filtered_df = df[:e_stamp]
elif sdate:
s_stamp = pd.Timestamp(sdate)
filtered_df = df[s_stamp:]
elif edate:
e_stamp = pd.Timestamp(edate)
filtered_df = df[:e_stamp]
else:
raise ValueError("You entered an invalid date to filter the table by or you did not include a date\n")
return filtered_df
if __name__ == '__main__':
if len(sys.argv) < 2:
print("An error occurred when you started the program. You must enter both a username and filename.")
sys.exit(1)
mft = CleanMFT(id = sys.argv(1), import_file = sys.argv(2))
mft.run