https://github.com/austin-taylor/flare
An analytical framework for network traffic and behavioral analytics
https://github.com/austin-taylor/flare
analytics cybersecurity domains elasticsearch network-analysis python
Last synced: about 2 months ago
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An analytical framework for network traffic and behavioral analytics
- Host: GitHub
- URL: https://github.com/austin-taylor/flare
- Owner: austin-taylor
- License: mit
- Created: 2016-11-10T00:14:39.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-12-07T23:39:04.000Z (over 2 years ago)
- Last Synced: 2025-03-29T19:08:43.422Z (about 2 months ago)
- Topics: analytics, cybersecurity, domains, elasticsearch, network-analysis, python
- Language: Python
- Homepage:
- Size: 55.3 MB
- Stars: 450
- Watchers: 38
- Forks: 86
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
---
Flare is a network analytic framework designed for data scientists, security researchers, and network professionals. Written in Python, it is designed for rapid prototyping and development of behavioral analytics, and intended to make identifying malicious behavior in networks as simple as possible.
Getting Started
---------------
Currently supports python 2.7 and python 3
```python
sudo pip install -r requirements.txt
python setup.py install
```
First Use
------------
Once **Flare** is installed you may use it via the command line by calling **flare_beacon**. You can use command line parameters or call a configuration file (recommended). See the configs directory for sample configuration files.
Example command below:
```bash
flare_beacon -c /path/to/flare/config/elasticsearch.ini --focus_outbound --whois flare_beacon -json /tmp/flare.json
```
Core Features
-------------
####
* Command and Control Analytics
* Identify Beaconing in your environment (works with Suricata output and ElasticSearch)
* Feature Extraction
* Helper utility functions to filter out the noise.
* Alexa, Umbrella, and Majestic Million (coming soon)
* WHOIS IP Lookup
* Pre-build machine learning classifiers
* So much more...
Analytics
=========
Beaconing
---------
Designed for elasticsearch and Suricata, elasticBeacon will connect to your elasticsearch server, retrieve all IP addresses and identify periodic activity.
You may need to forward port 9200 to your localhost with **ssh -NfL 9200:localhost:9200 [email protected]**
```python
from flare.analytics.command_control import elasticBeacon
eb = elasticBeacon(es_host='localhost')
beacons = eb.find_beacons(group=True, focus_outbound=True)
```
Also available in commandline:
```bash
CSV OUTPUT
flare_beacon --whois --focus_outbound -mo=100 --csv_out=beacon_results.csv
HTML OUTPUT
flare_beacon --group --whois --focus_outbound -c configs/elasticsearch.ini -html beacons.html
JSON OUTPUT (for SIEM)
flare_beacon --whois --focus_outbound -c /opt/flare-master/configs/selks4.ini -json beacon.json -v
```
Full writeup [here](http://www.austintaylor.io/detect/beaconing/intrusion/detection/system/command/control/flare/elastic/stack/2017/06/10/detect-beaconing-with-flare-elasticsearch-and-intrusion-detection-systems/)
Domain Features
===============
Alexa
-----
```python
from flare.tools.alexa import Alexa
alexa = Alexa(limit=1000000)
print alexa.domain_in_alexa('google.com') # Returns True
print alexa.subdomain_in_alexa('www') # Returns True
print alexa.DOMAINS_TOP1M #Displays domains (in this case top 100)
```
IP Utilities
------------
```python
from flare.tools.whoisip import WhoisLookup
whois = WhoisLookup()
whois.get_name_by_ip('8.8.8.8')
OUT: 'GOOGLE - Google Inc., US'
from flare.tools.iputils import hex_to_ip, ip_to_hex
ip_to_hex('8.8.8.8'), hex_to_ip('08080808')
OUT: (u'08080808', '8.8.8.8')
```
* Convert Hex to IP and vice/versa
* Check for Private, Multicast, or Reserved domains
* Identify the owner of a public IP address
Data Science Features
---------------------
```python
from flare.data_science.features import dga_classifier
dga_c = dga_classifier()
print dga_c.predict('facebook')
Legit
print dga_c.predict('39al31ak3')
dga
```
```python
from flare.data_science.features import entropy
from flare.data_science.features import ip_matcher
from flare.data_science.features import domain_extract
from flare.data_science.features import levenshtein
from flare.data_science.features import domain_tld_extract
# Entropy example
print entropy('akd93ka8a91a')
2.58496250072
# IP Matcher Example
print ip_matcher('8.8.8.8')
True
print ip_matcher('39.993.9.1')
False
# Domain Extract Example
domain_extract('longsubdomain.huntoperator.com')
'huntoperator'
# Domain TLD Extract
domain_tld_extract('longsubdomain.huntoperator.com')
'huntoperator.com'
# Levenshtein example
a = ['google.com']
b = ['googl3.com']
print levenshtein(a, b)
'Difference of:' 1
```
and many more features for data extraction...