Batea
A batea is a large shallow pan of wood or iron traditionally used by gold prospectors for washing sand and gravel to recover gold nuggets.
Batea is a context-driven network device ranking framework based on the anomaly detection family of machine learning algorithms. The goal of Batea is to allow security teams to automatically filter interesting network assets in large networks using nmap scan reports. We call those Gold Nuggets.
For more information about Gold Nuggeting and the science behind Batea, check out our whitepaper here
You can try Batea on your nmap scan data without downloading the software, using Batea Live: https://batea.delvesecurity.com/
How it works
Batea works by constructing a numerical representation (numpy) of all devices from your nmap reports (XML) and then applying anomaly detection methods to uncover the gold nuggets. It is easily extendable by adding specific features, or interesting characteristics, to the numerical representation of the network elements.
The numerical representation of the network is constructed using features, which are inspired by the expertise of the security community. The features act as elements of intuition, and the unsupervised anomaly detection methods allow the context of the network asset, or the total description of the network, to be used as the central building block of the ranking algorithm. The exact algorithm used is Isolation Forest (https://en.wikipedia.org/wiki/Isolation_forest)
Machine learning models are the heart of Batea. Models are algorithms trained on the whole dataset and used to predict a score on the same (and other) data points (network devices). Batea also allows for model persistence. That is, you can re-use pretrained models and export models trained on large datasets for further use.
Usage
# Complete info
$ sudo nmap -A 192.168.0.0/16 -oX output.xml
# Partial info
$ sudo nmap -O -sV 192.168.0.0/16 -oX output.xml
$ batea -v output.xml
Installation
$ git clone [email protected]:delvelabs/batea.git
$ cd batea
$ python3 setup.py sdist
$ pip3 install -r requirements.txt
$ pip3 install -e .
Developers Installation
$ git clone [email protected]:delvelabs/batea.git
$ cd batea
$ python3 -m venv batea/
$ source batea/bin/activate
$ python3 setup.py sdist
$ pip3 install -r requirements-dev.txt
$ pip3 install -e .
$ pytest
Example usage
# simple use (output top 5 gold nuggets with default format)
$ batea nmap_report.xml
# Output top 3
$ batea -n 3 nmap_report.xml
# Output all assets
$ batea -A nmap_report.xml
# Using multiple input files
$ batea -A nmap_report1.xml nmap_report2.xml
# Using wildcards (default xsl)
$ batea ./nmap*.xml
$ batea -f csv ./assets*.csv
# You can use batea on pretrained models and export trained models.
# Training, output and dumping model for persistence
$ batea -D mymodel.batea nmap_report.xml
# Using pretrained model
$ batea -L mymodel.batea nmap_report.xml
# Using preformatted csv along with xml files
$ batea -x nmap_report.xml -c portscan_data.csv
# Adjust verbosity
$ batea -vv nmap_report.xml
How to add a feature
Batea works by assigning numerical features to every host in the report (or series of report). Hosts are python objects derived from the nmap report. They consist of the following list of attributes: [ipv4, hostname, os_info, ports] where ports is a list of ports objects. Each port has the following list of attributes : [port, protocol, state, service, software, version, cpe, scripts], all defaulting to None.
Features are objects inherited from the FeatureBase class that instantiate a specific _transform method. This method always takes the list of all hosts as input and returns a lambda function that maps each host to a numpy column of numeric values (host order is conserved). The column is then appended to the matrix representation of the report. Features must output correct numerical values (floats or integers) and nothing else.
Most feature transformations are implemented using a simple lambda function. Just make sure to default a numeric value to every host for model compatibility.
Ex:
class CustomInterestingPorts(FeatureBase):
def __init__(self):
super().__init__(name="some_custom_interesting_ports")
def _transform(self, hosts):
"""This method takes a list of hosts and returns a function that counts the number
of host ports member from a predefined list of "interesting" ports, defaulting to 0.
Parameters
----------
hosts : list
The list of all hosts
Returns
-------
f : lambda function
Counts the number of ports in the defined list.
"""
member_ports = [21, 22, 25, 8080, 8081, 1234]
f = lambda host: len([port for port in host.ports if port.port in member_ports])
return f
You can then add the feature to the report by using the NmapReport.add_feature method in batea/__init__.py
from .features.basic_features import CustomInterestingPorts
def build_report():
report = NmapReport()
#[...]
report.add_feature(CustomInterestingPorts())
return report
Using precomputed tabular data (CSV)
It is possible to use preprocessed data to train the model or for prediction. The data has to be indexed by (ipv4, port) with one unique combination per row. The type of data should be close to what you expect from the XML version of an nmap report. A column has to use one of the following names, but you don't have to use all of them. The parser defaults to null values if a column is absent.
'ipv4',
'hostname',
'os_name',
'port',
'state',
'protocol',
'service',
'software_banner',
'version',
'cpe',
'other_info'
Example:
ipv4,hostname,os_name,port,state,protocol,service,software_banner
10.251.53.100,internal.delvesecurity.com,Linux,110,open,tcp,rpcbind,"program version port/proto service100000 2,3,4 111/tcp rpcbind100000 2,3,4 "
10.251.53.100,internal.delvesecurity.com,Linux,111,open,tcp,rpcbind,
10.251.53.188,serious.delvesecurity.com,Linux,6000,open,tcp,X11,"X11Probe: CentOS"
Outputing numerical representation
For the data scientist in you, or just for fun and profit, you can output the numerical matrix along with the score column instead of the regular output. This can be useful for further data analysis and debug purpose.
$ batea -oM network_matrix nmap_report.xml
9 Dec 7, 2022
14.5k Jan 8, 2023
60 Dec 31, 2022
63 Sep 27, 2022
401 Dec 23, 2022
21 Nov 9, 2022
47 Dec 16, 2022
95 Dec 4, 2022
4 Dec 27, 2022
6.9k Jan 4, 2023
3.8k Dec 22, 2022
655 Jan 4, 2023
5 Jun 16, 2022
28 Nov 29, 2022
2.6k Jan 4, 2023
14 Dec 17, 2021