Delve has made available a free tool that
rapidly generates a list of assets on any network that are likely to grab the
attention of an experienced hacker as attractive targets for initial compromise
from which to launch an attack.
"Experienced pentesters are able to identify assets on very large
networks that are unique in some way, and these 'outstanding' assets are often
prime targets for compromise for a number of reasons," noted Serge-Olivier
Paquette, Delve's Lead AI Researcher. "Using machine learning and
other AI techniques, we can simulate the 'intuition' of an experienced
pentester - or hacker - to automate that experience, and reveal which assets on
an enterprise network are of most interest for the wrong reasons."
Named "Batea" after the traditional pan used by gold miners to
extract gold nuggets from sand, Delve's Batea leverages machine learning
techniques to separate unremarkable network assets from those that are likely
to attract the most attention from a bad actor...the "gold nuggets."
"Identifying outlier assets on our customers' networks is just one of
the nearly 3 dozen factors we use when ranking the remediation priority of a
given vulnerability in a given network environment, but it's an important
one," added Pierre-David Oriol, Delve's VP of Product Management. "We
felt offering that one element of our product as a free tool would not only
benefit the cyber security community, but also help us improve its results
overall through its ability to train and share ML models over time."
Batea Live is now generally available to the public, and can be accessed at
delvesecurity.com/batea. A white paper detailing Batea's approach to outstanding
asset identification can be downloaded at https://delvesecurity.com/resources/automating-intuition-applying-machine-learning-to-outstanding-network-asset-detection/