  The concept of Industry 4.0 brings a disruption into the processing industry.
It is characterised by a high degree of intercommunication, embedded
computation, resulting in a decentralised and distributed handling of data.
Additionally, cloud-storage and Software-as-a-Service (SaaS) approaches enhance
a centralised storage and handling of data. This often takes place in
third-party networks. Furthermore, Industry 4.0 is driven by novel business
cases. Lot sizes of one, customer individual production, observation of process
state and progress in real-time and remote maintenance, just to name a few. All
of these new business cases make use of the novel technologies. However, cyber
security has not been an issue in industry. Industrial networks have been
considered physically separated from public networks. Additionally, the high
level of uniqueness of any industrial network was said to prevent attackers
from exploiting flaws. Those assumptions are inherently broken by the concept
of Industry 4.0. As a result, an abundance of attack vectors is created. In the
past, attackers have used those attack vectors in spectacular fashions.
Especially Small and Mediumsized Enterprises (SMEs) in Germany struggle to
adapt to these challenges. Reasons are the cost required for technical
solutions and security professionals. In order to enable SMEs to cope with the
growing threat in the cyberspace, the research project IUNO Insec aims at
providing and improving security solutions that can be used without specialised
security knowledge. The project IUNO Insec is briefly introduced in this work.
Furthermore, contributions in the field of intrusion detection, especially
machine learning-based solutions, for industrial environments provided by the
authors are presented and set into context.
