Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data

• Published in: arXiv.org
• Main Authors: Simon Duque Anton, Ahrens, Lia, Fraunholz, Daniel, Schotten, Hans Dieter
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 20.09.2018
• Subjects: Algorithms; Computer Science - Cryptography and Security; Computer Science - Learning; Cybersecurity; Encryption; Industrial applications; Intrusion detection systems; Machine learning; Neural networks; Parameterization; Protocol (computers); Short term; Statistics - Machine Learning; Time series
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1809.07500

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.