28 research outputs found
Discussing the Feasibility of Acoustic Sensors for Side Channel-aided Industrial Intrusion Detection: An Essay
The fourth industrial revolution leads to an increased use of embedded
computation and intercommunication in an industrial environment. While reducing
cost and effort for set up, operation and maintenance, and increasing the time
to operation or market respectively as well as the efficiency, this also
increases the attack surface of enterprises. Industrial enterprises have become
targets of cyber criminals in the last decade, reasons being espionage but also
politically motivated. Infamous attack campaigns as well as easily available
malware that hits industry in an unprepared state create a large threat
landscape. As industrial systems often operate for many decades and are
difficult or impossible to upgrade in terms of security, legacy-compatible
industrial security solutions are necessary in order to create a security
parameter. One plausible approach in industry is the implementation and
employment of side-channel sensors. Combining readily available sensor data
from different sources via different channels can provide an enhanced insight
about the security state. In this work, a data set of an experimental
industrial set up containing side channel sensors is discussed conceptually and
insights are derived
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data
The Industrial Internet of Things drastically increases connectivity of
devices in industrial applications. In addition to the benefits in efficiency,
scalability and ease of use, this creates novel attack surfaces. Historically,
industrial networks and protocols do not contain means of security, such as
authentication and encryption, that are made necessary by this development.
Thus, industrial IT-security is needed. In this work, emulated industrial
network data is transformed into a time series and analysed with three
different algorithms. The data contains labeled attacks, so the performance can
be evaluated. Matrix Profiles perform well with almost no parameterisation
needed. Seasonal Autoregressive Integrated Moving Average performs well in the
presence of noise, requiring parameterisation effort. Long Short Term
Memory-based neural networks perform mediocre while requiring a high training-
and parameterisation effort.Comment: Extended version of a publication in the 2018 IEEE International
Conference on Data Mining Workshops (ICDMW