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zeek_anomaly_detector.py
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zeek_anomaly_detector.py
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#!/usr/bin/env python3
# This file is part of the Stratosphere Linux IPS
# See the file 'LICENSE' for copying permission.
# Authors:
# - Sebastian Garcia. [email protected],
# - Veronica Valeros. [email protected]
import pandas as pd
# from sklearn.model_selection import train_test_split
# from pyod.models import lof
# from pyod.models.abod import ABOD
# from pyod.models.cblof import CBLOF
# from pyod.models.lof import LOF
# from pyod.models.loci import LOCI
# from pyod.models.lscp import LSCP
# from pyod.models.mcd import MCD
# from pyod.models.ocsvm import OCSVM
from pyod.models.pca import PCA
# from pyod.models.sod import SOD
# from pyod.models.so_gaal import SO_GAAL # Needs keras
# from pyod.models.sos import SOS # Needs keras
# from pyod.models.xgbod import XGBOD # Needs keras
# from pyod.models.knn import KNN # kNN detector
import argparse
import warnings
# This horrible hack is only to stop sklearn from printing those warnings
def warn(*args, **kwargs):
pass
warnings.warn = warn
def detect(file, amountanom, realtime, dumptocsv):
"""
Function to apply a very simple anomaly detector
amountanom: The top number of anomalies we want to print
realtime: If we want to read the conn.log file in real time (not working)
"""
# Create a Pandas dataframe from the conn.log
bro_df = pd.read_csv(file, sep="\t", comment='#', names=['ts', 'uid', 'id.orig_h', 'id.orig_p', 'id.resp_h', 'id.resp_p', 'proto', 'service', 'duration', 'orig_bytes', 'resp_bytes', 'conn_state', 'local_orig', 'local_resp', 'missed_bytes', 'history', 'orig_pkts', 'orig_ip_bytes', 'resp_pkts', 'resp_ip_bytes', 'tunnel_parents'])
# In case you need a label, due to some models being able to work in a
# semisupervized mode, then put it here. For now everything is
# 'normal', but we are not using this for detection
bro_df['label'] = 'normal'
# Replace the rows without data (with '-') with 0.
# Even though this may add a bias in the algorithms,
# is better than not using the lines.
# Also fill the no values with 0
# Finally put a type to each column
bro_df['orig_bytes'].replace('-', '0', inplace=True)
bro_df['orig_bytes'] = bro_df['orig_bytes'].fillna(0).astype('int32')
bro_df['resp_bytes'].replace('-', '0', inplace=True)
bro_df['resp_bytes'] = bro_df['resp_bytes'].fillna(0).astype('int32')
bro_df['resp_pkts'].replace('-', '0', inplace=True)
bro_df['resp_pkts'] = bro_df['resp_pkts'].fillna(0).astype('int32')
bro_df['orig_ip_bytes'].replace('-', '0', inplace=True)
bro_df['orig_ip_bytes'] = bro_df['orig_ip_bytes'].fillna(0).astype('int32')
bro_df['resp_ip_bytes'].replace('-', '0', inplace=True)
bro_df['resp_ip_bytes'] = bro_df['resp_ip_bytes'].fillna(0).astype('int32')
bro_df['duration'].replace('-', '0', inplace=True)
bro_df['duration'] = bro_df['duration'].fillna(0).astype('float64')
# Save dataframe to disk as CSV
if dumptocsv != "None":
bro_df.to_csv(dumptocsv)
# Add the columns from the log file that we know are numbers. This is only for conn.log files.
X_train = bro_df[['duration', 'orig_bytes', 'id.resp_p', 'resp_bytes', 'orig_ip_bytes', 'resp_pkts', 'resp_ip_bytes']]
# Our y is the label. But we are not using it now.
y = bro_df.label
# The X_test is where we are going to search for anomalies. In our case, its the same set of data than X_train.
X_test = X_train
#################
# Select a model from below
# ABOD class for Angle-base Outlier Detection. For an observation, the
# variance of its weighted cosine scores to all neighbors could be
# viewed as the outlying score.
# clf = ABOD()
# LOF
# clf = LOF()
# CBLOF
# clf = CBLOF()
# LOCI
# clf = LOCI()
# LSCP
# clf = LSCP()
# MCD
# clf = MCD()
# OCSVM
# clf = OCSVM()
# PCA. Good and fast!
clf = PCA()
# SOD
# clf = SOD()
# SO_GAAL
# clf = SO_GALL()
# SOS
# clf = SOS()
# XGBOD
# clf = XGBOD()
# KNN
# Good results but slow
# clf = KNN()
# clf = KNN(n_neighbors=10)
#################
# extract the value of dataframe to matrix
X_train = X_train.values
# Fit the model to the train data
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# Convert the ndarrays of scores and predictions to pandas series
scores_series = pd.Series(y_test_scores)
pred_series = pd.Series(y_test_pred)
# Now use the series to add a new column to the X test
X_test['score'] = scores_series.values
X_test['pred'] = pred_series.values
# Add the score to the bro_df also. So we can show it at the end
bro_df['score'] = X_test['score']
# Keep the positive predictions only. That is, keep only what we predict is an anomaly.
X_test_predicted = X_test[X_test.pred == 1]
# Keep the top X amount of anomalies
top10 = X_test_predicted.sort_values(by='score', ascending=False).iloc[:amountanom]
# Print the results
# Find the predicted anomalies in the original bro dataframe, where the rest of the data is
df_to_print = bro_df.iloc[top10.index]
print('\nFlows of the top anomalies')
# Only print some columns, not all, so its easier to read.
df_to_print = df_to_print.drop(['conn_state', 'history', 'local_orig', 'local_resp', 'missed_bytes', 'ts', 'tunnel_parents', 'uid', 'label'], axis=1)
print(df_to_print)
if __name__ == '__main__':
print('Simple Anomaly Detector for Zeek conn.log files. Version: 0.2')
print('Author: Sebastian Garcia ([email protected]), Veronica Valeros ([email protected])')
# Parse the parameters
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--verbose', help='Amount of verbosity. This shows more info about the results.', action='store', required=False, type=int)
parser.add_argument('-e', '--debug', help='Amount of debugging. This shows inner information about the program.', action='store', required=False, type=int)
parser.add_argument('-f', '--file', help='Path to the conn.log input file to read.', required=True)
parser.add_argument('-a', '--amountanom', help='Amount of anomalies to show.', required=False, default=10, type=int)
parser.add_argument('-R', '--realtime', help='Read the conn.log in real time.', required=False, type=bool, default=False)
parser.add_argument('-D', '--dumptocsv', help='Dump the conn.log DataFrame to a csv file', required=False)
args = parser.parse_args()
detect(args.file, args.amountanom, args.realtime, args.dumptocsv)