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.
Currently supports python 2.7 and python 3
sudo pip install -r requirements.txt
python setup.py installOnce 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:
flare_beacon -c /path/to/flare/config/elasticsearch.ini --focus_outbound --whois flare_beacon -json /tmp/flare.json- 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...
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 user@x.x.x.x
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:
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
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)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
from flare.data_science.features import dga_classifier
dga_c = dga_classifier()
print dga_c.predict('facebook')
Legit
print dga_c.predict('39al31ak3')
dgafrom 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:' 1and many more features for data extraction...