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