PrefixSpan/keris.ipynb/PrefixSpan_edit_20210925.py

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

# <p>NTM(유해트래픽 탐지장비)</p>
# <p>MTM(악성파일 탐지장비)</p>

# In[1]:


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

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

# load ts_data_accident-2020_sample.csv
# to prevent dtypewarning, set low_memory=False
df = pd.read_csv('ts_data_accident-2020_sample.csv', low_memory=False)
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) #len(df) : 10000, load successful
df.head()


# In[2]:


##################### NTM section #####################
NTM_df=df[df['ACCD_FIND_MTD_CODE']==1] #* edit'1' to 1
len(NTM_df)
#*NTM_df.head()


# In[3]:


# 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)

#* 추가 : 기존 filter_assets_value 사용시 값을 인식하지 못하는 문제 발생 -> RISK_V2를 별도의 df로 수정
import json
from pandas import json_normalize
risk_df = pd.DataFrame()
for newVal in RISK_V2_FILTERED:
    newVal = newVal.replace("'", "\"")
    newVal_str = json.loads(newVal)
    newVal_df = json_normalize(newVal_str) 
    risk_df = pd.concat([risk_df,newVal_df],ignore_index=True) 
    
risk_df_col = risk_df.columns.values.tolist()


# In[4]:


# In[352]:
asset_val = []
intent_val=[]
source_val=[]
def filter_assets_value(risk):
    for i in range(len(risk)):
        risks=[]
        intents=[]
        sources=[]
        try:
            for key in risk_df_col:
                if 'ASSETS_VAL_' in key and risk.iloc[i][key]:
                    risk_key_desc = 'RISK_V2.' + key + '=' + get_asset_desc(key)
                    risks.append(risk_key_desc)
                if 'INTENT_VAL_' in key and risk.iloc[i][key]:
                    intent_key_desc = 'RISK_V2.' + key + '=' + get_intent_desc(key)
                    intents.append(intent_key_desc)
                if 'SOURCE_VAL_' in key and risk.iloc[i][key]:
                    source_key_desc='RISK_V2.' + key + '=' + get_source_desc(key)
                    sources.append(source_key_desc)
        except:
            print(risk)
            print(type(risk))
        finally:
            asset_val.append(risks)
            intent_val.append(intents)
            source_val.append(sources)
    
    
# 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 ''



# 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[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[5]:


filter_assets_value(risk_df)
#뒤에 isna()를 통해 na값을 0으로 바꿔주는 작업을 하므로, 값이 비어있는 경우 [] 대신 비워두기
# New assets column
NTM_df['ASSETS_VAL']= asset_val
NTM_df['ASSETS_VAL']=NTM_df['ASSETS_VAL'].astype(str)
NTM_df['ASSETS_VAL']=NTM_df['ASSETS_VAL'].str.replace('[','', regex=False)
NTM_df['ASSETS_VAL']=NTM_df['ASSETS_VAL'].str.replace(']','', regex=False)
NTM_df[:1]
# New column of intent value
NTM_df['INTENT_VAL']=intent_val
NTM_df['INTENT_VAL']=NTM_df['INTENT_VAL'].astype(str)
NTM_df['INTENT_VAL']=NTM_df['INTENT_VAL'].str.replace('[','',regex=False)
NTM_df['INTENT_VAL']=NTM_df['INTENT_VAL'].str.replace(']','',regex=False)
NTM_df[:1]
# New column of SOURCE_VAL value
NTM_df['SOURCE_VAL']=source_val
NTM_df['SOURCE_VAL']=NTM_df['SOURCE_VAL'].astype(str)
NTM_df['SOURCE_VAL']=NTM_df['SOURCE_VAL'].str.replace('[','',regex=False)
NTM_df['SOURCE_VAL']=NTM_df['SOURCE_VAL'].str.replace(']','',regex=False)
NTM_df[:5]


# In[ ]:





# In[6]:


# In[361]:


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

# In[362]:
#NTM_df


# In[ ]:





# In[7]:


##################### 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()


# 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,'')


# 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]

# 수정하여 merge한 부분
NTM_new_com= []
for i in range(0,len(NTM_df)):
    temp_list = []
    temp_list.append([NTM_df['INST_NM'][i],NTM_df['TW_ATT_IP'][i],NTM_df['TW_ATT_PORT'][i], NTM_df['TW_DMG_IP'][i],
                      NTM_df['TW_DMG_PORT'][i], NTM_df['ACCD_DMG_PROTO_NM'][i], NTM_df['TW_ATT_CT_NM'][i], NTM_df['ASSETS_VAL'].loc[i], 
                      NTM_df['INTENT_VAL'].loc[i], NTM_df['SOURCE_VAL'].loc[i], NTM_df['DRULE_ATT_TYPE_CODE1'][i], NTM_df['DRULE_NM'][i]])
    NTM_new_com.extend(temp_list)
    

# Change the type to DataFrame
NTM_new_to_df=pd.DataFrame(NTM_new_com)
NTM_new_to_df[:5]
NTM_new_to_df.head()


# In[8]:


# Edit
NTM_new_tolist=NTM_new_to_df.values.tolist()
NTM_new_tolist[:2]


# In[9]:


from prefixspan import PrefixSpan
# In[370]:
# Apply prefixspan
PrefixSpan_NTM = PrefixSpan(NTM_new_tolist)
prefix_NTM=PrefixSpan_NTM.frequent(1)
prefix_NTM_df=pd.DataFrame(prefix_NTM)
prefix_NTM_df[:5]


# In[17]:


# 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']]


# 모든 가능한 조합에 대한 시나리오 Frequency 큰 값부터 정렬
prefix_NTM_df= prefix_NTM_df.sort_values(by=['Frequency'],ascending=False,ignore_index=True)


# In[ ]:


# In[373]:


# Define the function that find the rule name 
def generate_cause(cell):
    drules=['Attack','DDOS','HACK','MAIL','Malwr','WEB']
    for i in range(len(prefix_NTM_df)):
        for drule in drules:
            temp_drule = cell.iloc[i]['Cause']
            if drule in temp_drule:
                prefix_NTM_df.iloc[i]['Effect'] = drule


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


# In[ ]:


# 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)


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