parse log auth.log

master
Steve Nyemba 12 months ago
parent 5834a10099
commit 0738dc6649

@ -23,6 +23,28 @@ import shutil
from datetime import datetime
_cli = typer.Typer()
@_cli.command(name='log-intruder')
def intrusion(path:str='/var/log/auth.log', year:int=datetime.now().year):
"""
This function
"""
_r = smart.logger.read(path=path,year=year)
if _r :
for _id in _r :
if hasattr(smart.logger,_id):
try:
_pointer = getattr(smart.logger,_id)
_df = _pointer(_r[_id])
post(_df,_id)
except Exception as e:
print (e)
pass
else:
print ()
print ("Nothing out of the ordinary was found in")
print (f"{path}")
@_cli.command(name='top')
def apply_apps (app:str=None,user:str=None):
"""

@ -1,52 +1,77 @@
"""
This file looks into the logs to determine if there is any intrusion or provides means to assess logs
"""
import pandas as pd
import numpy as np
import transport
import datetime
import io
import json
import requests
import re
from datetime import datetime
def subscribe (self,**args) :
"""
This function will subscribe an email to a given service (report,notification). If already susbcribed no further action will be performed
:email provide a valid email for the free plan. Upgrades will be done via the website
:id service identifier accepted values are GOOGLE_DRIVE,DROPBOX,BOX,ONE_DRIVE
_date = "(^[A-Z][a-z]{2}) ([0-9]{2}) ([0-9]{2})\:([0-9]){2}\:([0-9]{2})"
_ip = "\d+\.\d+\.\d+\.\d+"
_regex = {
'login':{'pattern':f'{_date} .*Accepted password for ([a-z]+) from ({_ip})', 'columns':['month','day','hour','minute','second','user','ip']},
'attacks':{'pattern':f'{_date} .*Invalid user ([a-z,0-6]+) from ({_ip})','columns':['month','day','hour','minute','second','user','ip']},
'risk':{'pattern':f'{_date} .*Failed password for ([a-z,0-6]+) from ({_ip})','columns':['month','day','hour','minute','second','user','ip']} #-- accounts at risk
}
_map = {'Jan':1,'Feb':2,'Mar':3,'Apr':4,'May':5,'Jun':6,'Jul':7,'Aug':8,'Sep':9,'Oct':10,'Nov':11,'Dec':12}
def risk (_content,_id='user'):
"""
url = "https://the-phi.com/store/smart-top/subscribe"
SERVICES=['GOOGLE','DROPBOX','BOX','ONE_DRIVE']
if args['id'].upper() in SERVICES :
data = {"email":args['email']}
requests.post(url,data=data)
pass
def log(**args) :
compute the risk associated with accounts given the counts, this should be indicated by the number of failed password attempts in a given time frame
"""
This function will write to a designated location provided a set of inputs
:store mongo,file,couch,api
_df = pd.DataFrame(_content)
_g = _df.groupby([_id]).apply(lambda row: {'start_date':row.date.min(),'end_date':row.date.max() ,'count':row[_id].size} )
_df = pd.DataFrame(_g.tolist())
_df['user'] = _g.index
_df.start_date = _df.start_date.astype(str)
_df.end_date = _df.end_date.astype(str)
return _df
def attacks (_content):
"""
#
# @TODO: Provide facility to write to a given cloud store (google,one-drive ...)
# This will have to be supported by some sort of subscription service
#
STORE_MAP = {"mongo":"MongoWriter","disk":"DiskWriter","couch":"CouchWriter",'sqlite':'SQLiteWriter'}
if 'store' not in args :
_id = 'console'
else:
_id = 'disk' if args['store'] == 'file' else args['store']
_id = 'disk' if _id == 'sqlite' else _id
if _id == 'console' :
This function will compute counts associated with a given set of ip addresses. If behind a load balancer IP can be ignored and counts will reflect break-in attempts
"""
We are going to print whatever we have to the console ... using the tool in cli mode
return risk(_content,'ip')
def login(_content):
return risk(_content,'user')
def read (**_args):
"""
print()
print (args['data'])
print ()
# stream = args['memory']
# stream.write(json.dumps(args['row']) if isinstance(args['row'],dict) else args['row'])
# stream.write("\n")
else:
store_type = ".".join([args['store'],STORE_MAP[_id]])
store_args = args['params']
store = transport.factory.instance(type=store_type,args=store_args)
store.write( args['row'])
:path path of the auth.log files to load
"""
_year = _args['year'] if 'year' in _args else datetime.now().year
_path = _args['path']
f = open(_path)
_content = f.read().split('\n')
f.close()
r = {}
for line in _content :
for _id in _regex :
_pattern = _regex[_id]['pattern']
_columns = _regex[_id]['columns']
_out = re.search(_pattern,line)
if _out :
try:
_object = dict(zip(_columns,_out.groups()[:]))
if _id not in r :
r[_id] = []
_month = _object['month']
if _month in _map :
_object['month'] = _map[ _month ]
for field in ['day','month','hour','minute','second'] :
_object[field] = int (_object[field])
_object['date'] = datetime ( year=_year,month=_object['month'], day=_object['day'], hour=_object['hour'],minute=_object['minute'],second=_object['second'])#'-'.join([str(_object['month']),str(_object['day'])]) + ' '+_object['time']
# _object['date'] = np.datetime64(_object['date'])
r[_id].append(_object)
except Exception as e:
print(e)
pass
#
# At this point we have essential information formatted
# Summarizing this information will serve as a means to compress it
#
return r
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