Publication
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data
Simon Duque Antón; Lia Ahrens; Daniel Fraunholz; Hans Dieter Schotten
In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE International Conference on Data Mining Workshops (ICDMW-2018), First IEEE International Workshop on Adapting Data Mining for Security 2018, located at IEEE International Conference on Data Mining, November 17-20, Singapore, Singapore, IEEE, 11/2018.
Abstract
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