#!/usr/bin/env python
# coding: utf-8

# In[1]:


import os
import array
import math
import pickle
# import joblib
import sys
import argparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from pprint import pprint
import ssl
from elasticsearch.connection import create_ssl_context
from elasticsearch import Elasticsearch
from elasticsearch import helpers
import urllib3


# In[3]:


import pandas as pd
import numpy as np
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import association_rules, fpgrowth
from prefixspan import PrefixSpan


# In[4]:


ssl_context = create_ssl_context()
ssl_context.check_hostname = False
ssl_context.verify_mode = ssl.CERT_NONE


# In[12]:


es = Elasticsearch(hosts=[{'host': '223.194.92.152', 'port': 9200}], scheme="http",verify_certs=False, timeout=300, ssl_context=ssl_context, http_auth=("elasticsearch", "hadoop2019@!@#$"))
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)


# In[347]:


######## 2020, 1 year ########
######## There are no MTM data in 2018, 2019 ########

body = {
         "size" : 10000,
         "query": {
                 "range":{
                    "TW_COLLECT_DT":{
                        "gte":"2020-01-01T00:00:00.625+09:00",
                        "lte":"2020-12-31T00:00:00.625+09:00" ################
                    }
                }
                 },
    "sort":[{
        "_id":"asc"
    }]
}
        
res = es.search(index = 'ts_data_accident-2020', body=body)
data = res['hits']['hits']
nxt=res["hit"]["hit"][-1]["sort"][0]
total = res['hits']['total']

# print(total)

accident = []
for da in data:
    att_type = da['_source']
    # att_type["POL_NM"]=att_type["SCEN_INFOS"][0]["POL_NM"]
    accident.append(att_type)

# df = pd.DataFrame(accident,dtype=str)
df_10000 = pd.DataFrame(accident)

print(df_10000.head())


# In[ ]:


######## 2020, 1 year ########
######## There are no MTM data in 2018, 2019 ########

body = {
         "size" : 10000,
         "search_after":[nxt],
         "query": {
                 "range":{
                    "TW_COLLECT_DT":{
                        "gte":"2020-01-01T00:00:00.625+09:00",
                        "lte":"2020-12-31T00:00:00.625+09:00" ################
                    }
                }
                 },
    "sort":[{
        "_id":"asc"
    }]
}
        
res = es.search(index = 'ts_data_accident-2020', body=body)
data = res['hits']['hits']
nxt=res["hit"]["hit"][-1]["sort"][0]
total = res['hits']['total']

# print(total)

accident = []
for da in data:
    att_type = da['_source']
    # att_type["POL_NM"]=att_type["SCEN_INFOS"][0]["POL_NM"]
    accident.append(att_type)

# df = pd.DataFrame(accident,dtype=str)
df_20000 = pd.DataFrame(accident)

print(df_20000.head())


# In[348]:


df=df[['RISK_V2','INST_NM','DRULE_ATT_TYPE_CODE1','TW_ATT_IP','TW_ATT_PORT','TW_DMG_IP','TW_DMG_PORT','ACCD_DMG_PROTO_NM','TW_ATT_CT_NM','ACCD_FIND_MTD_CODE','DRULE_NM']].dropna()
len(df)
df.head()


# In[349]:


##################### NTM section #####################


# In[350]:


NTM_df=df[df['ACCD_FIND_MTD_CODE']=='1']
len(NTM_df)


# In[351]:


# Pick out it in order to get the asset, risk, intent, black IP out
RISK_V2=NTM_df['RISK_V2']

RISK_V2_FILTERED=RISK_V2.dropna()
print(RISK_V2.size)
print(RISK_V2_FILTERED.size)





# In[352]:


def filter_assets_value(risk):
  risks=[]
  try:
    for risk_key in risk:
      if 'ASSETS_VAL_' in risk_key and risk[risk_key]:
        risk_key_desc = 'RISK_V2.' + risk_key + '=' + get_asset_desc(risk_key)
        risks.append(risk_key_desc)
  except:
    print(risk)
    print(type(risk))
  finally:
    return risks
  
  


# In[353]:


# modified
def get_asset_desc(asset_field):
  if asset_field == 'ASSETS_VAL_1':
    return '공인-전체IP대역(유선)'
  elif asset_field == 'ASSETS_VAL_2':
    return '공인-전체IP대역(무선)'
  elif asset_field == 'ASSETS_VAL_3':
    return '공인-WEB서버'
  elif asset_field == 'ASSETS_VAL_4':
    return '공인-내부응용서버'
  elif asset_field == 'ASSETS_VAL_5':
    return '공인-DB서버'
  elif asset_field == 'ASSETS_VAL_6':
    return '공인-패치서버'
  elif asset_field == 'ASSETS_VAL_7':
    return '공인-네트워크'
  elif asset_field == 'ASSETS_VAL_8':
    return '공인-보안'
  elif asset_field == 'ASSETS_VAL_9':
    return '공인-업무용PC'
  elif asset_field == 'ASSETS_VAL_10':
    return '공인-비업무용PC'
  elif asset_field == 'ASSETS_VAL_11':
    return '공인-기타'
  elif asset_field == 'ASSETS_VAL_12':
    return '사설-전체IP대역(유선)'
  elif asset_field == 'ASSETS_VAL_13':
    return '사설-전체IP대역(무선)'
  elif asset_field == 'ASSETS_VAL_14':
    return '사설-WEB서버'
  elif asset_field == 'ASSETS_VAL_15':
    return '사설-내부응용서버'
  elif asset_field == 'ASSETS_VAL_16':
    return '사설-DB서버'
  elif asset_field == 'ASSETS_VAL_17':
    return '사설-패치서버'
  elif asset_field == 'ASSETS_VAL_18':
    return '사설-네트워크'
  elif asset_field == 'ASSETS_VAL_19':
    return '사설-보안'
  elif asset_field == 'ASSETS_VAL_20':
    return '사설-업무용PC'
  elif asset_field == 'ASSETS_VAL_21':
    return '사설-비업무용PC'
  elif asset_field == 'ASSETS_VAL_22':
    return '사설-기타'
  else:
    return ''


# In[354]:


# New assets column
NTM_df['ASSETS_VAL']=list(map(filter_assets_value, RISK_V2_FILTERED))
NTM_df['ASSETS_VAL']=NTM_df['ASSETS_VAL'].astype(str)
NTM_df[:1]


# In[355]:


# modified
def filter_intent(intent):
  intents=[]
  for intent_key in intent:
    if 'INTENT_VAL_' in intent_key and intent[intent_key]:
     intent_key_desc = 'RISK_V2.' + intent_key + '=' + get_intent_desc(intent_key)
     intents.append(intent_key_desc)
  return intents


# In[356]:


def get_intent_desc(intent_field):
  if intent_field == 'INTENT_VAL_1':
    return '파괴'
  elif intent_field == 'INTENT_VAL_2':
    return '유출'
  elif intent_field == 'INTENT_VAL_3':
    return '지연'
  elif intent_field == 'INTENT_VAL_4':
    return '잠복'
  elif intent_field == 'INTENT_VAL_5':
    return '단순침입'
  elif intent_field == 'INTENT_VAL_6':
    return 'MD5'
  elif intent_field == 'INTENT_VAL_0':
    return 'Default'
  else:
    return ''


# In[357]:


# New column of intent value
NTM_df['INTENT_VAL']=list(map(filter_intent, RISK_V2_FILTERED))
NTM_df['INTENT_VAL']=NTM_df['INTENT_VAL'].astype(str)
NTM_df[:1]


# In[358]:


# modified
def filter_source(source):
  sources=[]
  for source_key in source:
    if 'SOURCE_VAL_' in source_key and source[source_key]:
      source_key_desc='RISK_V2.' + source_key + '=' + get_source_desc(source_key)
      sources.append(source_key_desc)
  return sources


# In[359]:


def get_source_desc(source_field):
  if source_field=='SOURCE_VAL_1':
    return '북한IP'
  if source_field=='SOURCE_VAL_3':
    return 'ECSC Black IP'
  else:
    return ''


# In[360]:


# New column of SOURCE_VAL value
NTM_df['SOURCE_VAL']=list(map(filter_source, RISK_V2_FILTERED))
NTM_df['SOURCE_VAL']=NTM_df['SOURCE_VAL'].astype(str)
NTM_df[:5]


# In[361]:


NTM_df.drop(columns=['RISK_V2'], inplace=True)
NTM_df.columns


# In[362]:


##################### 여기서부터 진행하시면 됩니다. #####################
##################### 아래 12개 아이템(12. 장비 ACCD_FIND_MTD_CODE 제외)에 대해서 모든 아이템 조합에 알고리즘 적용하기#####################

# It should be 13 columns in total

# 1. 기관 INST_NM
# 2. 공격 DRULE_ATT_TYPE_CODE1
# 3. 자산 ASSETS_VAL
# 4. 위협공격ip TW_ATT_IP
# 5. 위협공격port TW_ATT_PORT
# 6. 위협피해ip TW_DMG_IP
# 7. 위협피해port TW_DMG_PORT
# 8. 위협피해프로토콜 ACCD_DMG_PROTO_NM
# 9. 공격국가 TW_ATT_CT_NM
# 10. 의도(7개) INTENT_VAL
# 11. IP/URL 가중치 SOURCE_VAL
# 12. 장비 ACCD_FIND_MTD_CODE
# 13. 탐지규칙명 DRULE_NM


# 

# In[363]:


NTM_df.isna().sum()


# In[364]:


# Change the Nan to zero
NTM_df['ACCD_DMG_PROTO_NM']=NTM_df['ACCD_DMG_PROTO_NM'].replace(np.nan,'')
NTM_df['INST_NM']=NTM_df['INST_NM'].replace(np.nan,'')
NTM_df['DRULE_ATT_TYPE_CODE1']=NTM_df['DRULE_ATT_TYPE_CODE1'].replace(np.nan,'')
NTM_df['TW_ATT_IP']=NTM_df['TW_ATT_IP'].replace(np.nan,0)
NTM_df['TW_ATT_PORT']=NTM_df['TW_ATT_PORT'].replace(np.nan,0)
NTM_df['TW_DMG_IP']=NTM_df['TW_DMG_IP'].replace(np.nan,0)
NTM_df['TW_DMG_PORT']=NTM_df['TW_DMG_PORT'].replace(np.nan,0)
NTM_df['TW_ATT_CT_NM']=NTM_df['TW_ATT_CT_NM'].replace(np.nan,'')
NTM_df['ASSETS_VAL']=NTM_df['ASSETS_VAL'].replace(np.nan,0)
NTM_df['INTENT_VAL']=NTM_df['INTENT_VAL'].replace(np.nan,0)
NTM_df['SOURCE_VAL']=NTM_df['SOURCE_VAL'].replace(np.nan,0)
NTM_df['DRULE_NM']=NTM_df['DRULE_NM'].replace(np.nan,'')


# In[365]:


# Check NaN out again
NTM_df.isna().sum()


# In[366]:


# # Merge all

# # Make one string from all of elements
NTM_df['Combined']=NTM_df['INST_NM'].astype(str)+' '+NTM_df['TW_ATT_IP'].astype(str)
+' '+NTM_df['TW_ATT_PORT'].astype(str)+' '+NTM_df['TW_DMG_IP'].astype(str)+' '
+NTM_df['TW_DMG_PORT'].astype(str) +' '+NTM_df['ACCD_DMG_PROTO_NM'].astype(str)
+' '+NTM_df['TW_ATT_CT_NM']+' '+NTM_df['ASSETS_VAL']+' '+NTM_df['INTENT_VAL']+' '
+NTM_df['SOURCE_VAL']+' '+NTM_df['DRULE_ATT_TYPE_CODE1']+' '+NTM_df['DRULE_NM']

NTM_com=NTM_df['Combined']
NTM_com[:10]


# In[367]:


# Change the type to DataFrame
NTM_to_df=pd.DataFrame(NTM_com)
NTM_to_df[:5]


# In[368]:


# Change the type to list in order to apply the algorithm(nested list)
NTM_tolist=NTM_to_df.values.tolist()
NTM_tolist[:5]


# In[369]:


from prefixspan import PrefixSpan


# In[370]:


# Apply prefixspan
PrefixSpan_NTM = PrefixSpan(NTM_tolist)

###### Interchangeable ######
# Get any over frequency 1 
prefix_NTM=PrefixSpan_NTM.frequent(1)
prefix_NTM[:3]


# In[371]:


# Put the result to DataFrame
prefix_NTM_df=pd.DataFrame(prefix_NTM)
prefix_NTM_df[:5]


# In[372]:


# Change the columns name
prefix_NTM_df.rename(columns={0:'Frequency',1:'Cause'},inplace=True)

# Make the new column for filling the Effect
prefix_NTM_df['Effect']=np.nan

# Change the order of columns
prefix_NTM_df=prefix_NTM_df[['Cause','Effect','Frequency']]
prefix_NTM_df[:2]


# In[373]:


# Define the function that find the rule name 
def generate_cause(cell):
  drules=['Attack','DDOS','HACK','MAIL','Malwr','WEB']
  for drule in drules:
    if ' '+drule in cell[0]:
      return drule 
  return ''
      
# Mapping the rule name with cause that is the effect
effect=list(map(generate_cause, prefix_NTM_df.Cause))

# Assign the rule name as an effect
prefix_NTM_df['Effect']=effect
prefix_NTM_df.sort_values(by=['Frequency'],ascending=False)


# In[374]:


# Attack Filter
def Attack_filter(ps):
    return ' Attack' in ps[0]

att_filter=prefix_NTM_df[list(map(Attack_filter, prefix_NTM_df.Cause))].fillna('Attack')

# Malwr Filter
def Malwr_filter(ps):
    return ' Malwr' in ps[0]

mal_filter=prefix_NTM_df[list(map(Malwr_filter, prefix_NTM_df.Cause))].fillna('Malwr')

# DDOS Filter
def DDOS_filter(ps):
    return ' DDOS' in ps[0]

dd_filter=prefix_NTM_df[list(map(DDOS_filter, prefix_NTM_df.Cause))].fillna('DDOS')

# HACK Filter
def HACK_filter(ps):
    return ' HACK' in ps[0]

hack_filter=prefix_NTM_df[list(map(HACK_filter, prefix_NTM_df.Cause))].fillna('HACK')

# MAIL Filter
def MAIL_filter(ps):
    return ' MAIL' in ps[0]

mail_filter=prefix_NTM_df[list(map(MAIL_filter, prefix_NTM_df.Cause))].fillna('MAIL')

# WEB Filter
def WEB_filter(ps):
    return ' WEB' in ps[0]
prefix_NTM_df
web_filter=prefix_NTM_df[list(map(WEB_filter, prefix_NTM_df.Cause))].fillna('WEB')

frames = [att_filter, mal_filter, dd_filter, hack_filter, mail_filter, web_filter]
result = pd.concat(frames)
result.sort_values(by=['Frequency'],ascending=False)


# In[ ]:


##################### NTM section End #####################


# In[ ]:


##################### MTM section #####################


# In[375]:


MTM_df=df[df['ACCD_FIND_MTD_CODE']=='2']
len(MTM_df)


# In[376]:


# Pick out it in order to get the asset, risk, intent, black IP out
RISK_V2_MTM=MTM_df['RISK_V2']

RISK_V2_FILTERED_MTM=RISK_V2_MTM.dropna()
print(RISK_V2_MTM.size)
print(RISK_V2_FILTERED_MTM.size)


# In[377]:


def filter_assets_value_MTM(risk):
  risks=[]
  try:
    for risk_key in risk:
      if 'ASSETS_VAL_' in risk_key and risk[risk_key]:
        risk_key_desc = 'RISK_V2.' + risk_key + '=' + get_asset_desc(risk_key)
        risks.append(risk_key_desc)
  except:
    print(risk)
    print(type(risk))
  finally:
    return risks


# In[378]:


# modified
def get_asset_desc_MTM(asset_field):
  if asset_field == 'ASSETS_VAL_1':
    return '공인-전체IP대역(유선)'
  elif asset_field == 'ASSETS_VAL_2':
    return '공인-전체IP대역(무선)'
  elif asset_field == 'ASSETS_VAL_3':
    return '공인-WEB서버'
  elif asset_field == 'ASSETS_VAL_4':
    return '공인-내부응용서버'
  elif asset_field == 'ASSETS_VAL_5':
    return '공인-DB서버'
  elif asset_field == 'ASSETS_VAL_6':
    return '공인-패치서버'
  elif asset_field == 'ASSETS_VAL_7':
    return '공인-네트워크'
  elif asset_field == 'ASSETS_VAL_8':
    return '공인-보안'
  elif asset_field == 'ASSETS_VAL_9':
    return '공인-업무용PC'
  elif asset_field == 'ASSETS_VAL_10':
    return '공인-비업무용PC'
  elif asset_field == 'ASSETS_VAL_11':
    return '공인-기타'
  elif asset_field == 'ASSETS_VAL_12':
    return '사설-전체IP대역(유선)'
  elif asset_field == 'ASSETS_VAL_13':
    return '사설-전체IP대역(무선)'
  elif asset_field == 'ASSETS_VAL_14':
    return '사설-WEB서버'
  elif asset_field == 'ASSETS_VAL_15':
    return '사설-내부응용서버'
  elif asset_field == 'ASSETS_VAL_16':
    return '사설-DB서버'
  elif asset_field == 'ASSETS_VAL_17':
    return '사설-패치서버'
  elif asset_field == 'ASSETS_VAL_18':
    return '사설-네트워크'
  elif asset_field == 'ASSETS_VAL_19':
    return '사설-보안'
  elif asset_field == 'ASSETS_VAL_20':
    return '사설-업무용PC'
  elif asset_field == 'ASSETS_VAL_21':
    return '사설-비업무용PC'
  elif asset_field == 'ASSETS_VAL_22':
    return '사설-기타'
  else:
    return ''


# In[379]:


# New assets column
MTM_df['ASSETS_VAL']=list(map(filter_assets_value_MTM, RISK_V2_FILTERED_MTM))
MTM_df['ASSETS_VAL']=MTM_df['ASSETS_VAL'].astype(str)
MTM_df[:1]


# In[381]:


# modified
def filter_intent_MTM(intent):
  intents=[]
  for intent_key in intent:
    if 'INTENT_VAL_' in intent_key and intent[intent_key]:
     intent_key_desc = 'RISK_V2.' + intent_key + '=' + get_intent_desc(intent_key)
     intents.append(intent_key_desc)
  return intents


# In[382]:


def get_intent_desc_MTM(intent_field):
  if intent_field == 'INTENT_VAL_1':
    return '파괴'
  elif intent_field == 'INTENT_VAL_2':
    return '유출'
  elif intent_field == 'INTENT_VAL_3':
    return '지연'
  elif intent_field == 'INTENT_VAL_4':
    return '잠복'
  elif intent_field == 'INTENT_VAL_5':
    return '단순침입'
  elif intent_field == 'INTENT_VAL_6':
    return 'MD5'
  elif intent_field == 'INTENT_VAL_0':
    return 'Default'
  else:
    return ''


# In[383]:


# New column of intent value
MTM_df['INTENT_VAL']=list(map(filter_intent_MTM, RISK_V2_FILTERED_MTM))
MTM_df['INTENT_VAL']=MTM_df['INTENT_VAL'].astype(str)
MTM_df[:1]


# In[384]:


# modified
def filter_source_MTM(source):
  sources=[]
  for source_key in source:
    if 'SOURCE_VAL_' in source_key and source[source_key]:
      source_key_desc='RISK_V2.' + source_key + '=' + get_source_desc(source_key)
      sources.append(source_key_desc)
  return sources


# In[385]:


def get_source_desc_MTM(source_field):
  if source_field=='SOURCE_VAL_1':
    return '북한IP'
  if source_field=='SOURCE_VAL_3':
    return 'ECSC Black IP'
  else:
    return ''


# In[386]:


# New column of SOURCE_VAL value
MTM_df['SOURCE_VAL']=list(map(filter_source_MTM, RISK_V2_FILTERED_MTM))
MTM_df['SOURCE_VAL']=MTM_df['SOURCE_VAL'].astype(str)
MTM_df[:5]


# In[387]:


MTM_df.drop(columns=['RISK_V2'], inplace=True)
MTM_df.columns


# In[388]:


MTM_df.isna().sum()


# In[389]:


# Change the Nan to zero
MTM_df['ACCD_DMG_PROTO_NM']=MTM_df['ACCD_DMG_PROTO_NM'].replace(np.nan,'')
MTM_df['INST_NM']=MTM_df['INST_NM'].replace(np.nan,'')
MTM_df['DRULE_ATT_TYPE_CODE1']=MTM_df['DRULE_ATT_TYPE_CODE1'].replace(np.nan,'')
MTM_df['TW_ATT_IP']=MTM_df['TW_ATT_IP'].replace(np.nan,0)
MTM_df['TW_ATT_PORT']=MTM_df['TW_ATT_PORT'].replace(np.nan,0)
MTM_df['TW_DMG_IP']=MTM_df['TW_DMG_IP'].replace(np.nan,0)
MTM_df['TW_DMG_PORT']=MTM_df['TW_DMG_PORT'].replace(np.nan,0)
MTM_df['TW_ATT_CT_NM']=MTM_df['TW_ATT_CT_NM'].replace(np.nan,'')
MTM_df['ASSETS_VAL']=MTM_df['ASSETS_VAL'].replace(np.nan,0)
MTM_df['INTENT_VAL']=MTM_df['INTENT_VAL'].replace(np.nan,0)
MTM_df['SOURCE_VAL']=MTM_df['SOURCE_VAL'].replace(np.nan,0)
MTM_df['DRULE_NM']=MTM_df['DRULE_NM'].replace(np.nan,'')


# In[390]:


# Check NaN out again
MTM_df.isna().sum()


# In[391]:


# # Merge all

# # Make one string from all of elements
MTM_df['Combined']=MTM_df['INST_NM'].astype(str)+' '+MTM_df['TW_ATT_IP'].astype(str)+' '+MTM_df['TW_ATT_PORT'].astype(str)+' '+MTM_df['TW_DMG_IP'].astype(str)+' '+MTM_df['TW_DMG_PORT'].astype(str) +' '+MTM_df['ACCD_DMG_PROTO_NM'].astype(str)+' '+MTM_df['TW_ATT_CT_NM']+' '+MTM_df['ASSETS_VAL']+' '+MTM_df['INTENT_VAL']+' '+MTM_df['SOURCE_VAL']+' '+MTM_df['DRULE_ATT_TYPE_CODE1']+' '+MTM_df['DRULE_NM']

MTM_com=MTM_df['Combined']
MTM_com[:10]


# In[392]:


# Change the type to DataFrame
MTM_to_df=pd.DataFrame(MTM_com)
MTM_to_df[:5]


# In[393]:


# Change the type to list in order to apply the algorithm(nested list)
MTM_tolist=MTM_to_df.values.tolist()
MTM_tolist[:5]


# In[394]:


# Apply prefixspan
PrefixSpan_MTM = PrefixSpan(MTM_tolist)

###### Interchangeable ######
# Get any over frequency 1 
prefix_MTM=PrefixSpan_MTM.frequent(1)
prefix_MTM[:3]


# In[395]:


# Put the result to DataFrame
prefix_MTM_df=pd.DataFrame(prefix_MTM)
prefix_MTM_df[:5]


# In[396]:


# Change the columns name
prefix_MTM_df.rename(columns={0:'Frequency',1:'Cause'},inplace=True)

# Make the new column for filling the Effect
prefix_MTM_df['Effect']=np.nan

# Change the order of columns
prefix_MTM_df=prefix_MTM_df[['Cause','Effect','Frequency']]
prefix_MTM_df[:2]


# In[397]:


# Define the function that find the rule name 
def generate_cause_MTM(cell):
  drules=['Attack','DDOS','HACK','MAIL','Malwr','WEB']
  for drule in drules:
    if ' '+drule in cell[0]:
      return drule 
  return ''
      
# Mapping the rule name with cause that is the effect
effect_MTM=list(map(generate_cause, prefix_MTM_df.Cause))

# Assign the rule name as an effect
prefix_MTM_df['Effect']=effect_MTM
prefix_MTM_df.sort_values(by=['Frequency'],ascending=False)


# In[399]:


# Attack Filter
def Attack_filter_MTM(ps):
    return ' Attack' in ps[0]

att_filter_MTM=prefix_MTM_df[list(map(Attack_filter_MTM, prefix_MTM_df.Cause))].fillna('Attack')

# Malwr Filter
def Malwr_filter_MTM(ps):
    return ' Malwr' in ps[0]

mal_filter_MTM=prefix_MTM_df[list(map(Malwr_filter_MTM, prefix_MTM_df.Cause))].fillna('Malwr')

# DDOS Filter
def DDOS_filter_MTM(ps):
    return ' DDOS' in ps[0]

dd_filter_MTM=prefix_MTM_df[list(map(DDOS_filter_MTM, prefix_MTM_df.Cause))].fillna('DDOS')

# HACK Filter
def HACK_filter_MTM(ps):
    return ' HACK' in ps[0]

hack_filter_MTM=prefix_MTM_df[list(map(HACK_filter_MTM, prefix_MTM_df.Cause))].fillna('HACK')

# MAIL Filter
def MAIL_filter_MTM(ps):
    return ' MAIL' in ps[0]

mail_filter_MTM=prefix_MTM_df[list(map(MAIL_filter_MTM, prefix_MTM_df.Cause))].fillna('MAIL')

# WEB Filter
def WEB_filter_MTM(ps):
    return ' WEB' in ps[0]

prefix_MTM_df[:5]
web_filter_MTM=prefix_MTM_df[list(map(WEB_filter_MTM, prefix_MTM_df.Cause))].fillna('WEB')

frames_MTM = [att_filter_MTM, mal_filter_MTM, dd_filter_MTM, hack_filter_MTM, mail_filter_MTM, web_filter_MTM]
result_MTM = pd.concat(frames_MTM)
result_MTM.sort_values(by=['Frequency'],ascending=False)


# In[ ]: