author = "Jasenko Hosic and Jereme Lamps and Derek H. Hart",
booktitle = "2015 World Congress on Industrial Control Systems
Security (WCICSS)",
title = "Evolving decision trees to detect anomalies in
recurrent ICS networks",
year = "2015",
pages = "50--57",
abstract = "Researchers have previously attempted to apply machine
learning techniques to network anomaly detection
problems. Due to the staggering amount of variety that
can occur in normal networks, as well as the difficulty
in capturing realistic data sets for supervised
learning or testing, the results have often been
underwhelming. These challenges are far less pronounced
when considering industrial control system (ICS)
networks. The recurrent nature of these networks
results in less noise and more consistent patterns for
a machine learning algorithm to recognise. We propose a
method of evolving decision trees through genetic
programming (GP) in order to detect network anomalies,
such as device outages. Our approach extracts over a
dozen features from network packet captures and
netflows, normalizes them, and relates them in decision
trees using fuzzy logic operators. We used the trees to
detect three specific network events from three
different points on the network across a statistically
significant number of runs and achieved 100percent
accuracy on five of the nine experiments. When the
trees attempted to detect more challenging events at
points of presence further from the occurrence, the
accuracy averaged to above 98percent. On cases where
the trees were many hops away and not enough
information was available, the accuracy dipped to
roughly 50percent, or that of a random search. Using
our method, all of the evolutionary cycles of the GP
algorithm are computed a-priori, allowing the best
resultant trees to be deployed as semi-real-time
sensors with little overhead. In order for the trees to
perform optimally, buffered packets and flows need to
be ingested at twenty minute intervals.",