As 5G is currently being rolled out, security considerations for this critical infrastructure are getting more into focus. Hereby, the security investigation of the 5G core as the central element plays a pivotal role. The structure of the core is based on a Service-Based Architecture (SBA) consisting of Network Functions (NFs). These NFs communicate via REST/HTTP2 interfaces, that can be secured using Transport Layer Security (TLS) for encryption. However, this enhanced security is not enforced by standardization, but up to the system operator to decide. Therefore, in this work we derive recommendations on when to use TLS. For that, we investigate the overhead of TLS in a simulation, based on the open-source frameworks Open5GS and UERANSIM. To measure a user-relevant overhead, we look into 5G's UE registration and Packet Data Unit (PDU) session establishment procedures. By testing 14 of the most relevant cipher suites, our results show, that TLS adds no more than 1\,% of time overhead in a running system. Further, we show cipher suites using ECDSA keys to be faster than the ones using RSA keys. Surprisingly, TLS 1.3 shows a larger performance overhead than its predecessor TLS 1.2. We demonstrate CPU and memory overhead of TLS to be insignificant in the context of the 5G core.

Operational Technology (OT) networks and devices, i.e., all components used in industrial environments, were not designed with security in mind. Efficiency and ease of use were the most important design characteristics. However, due to the digitization of industry, an increasing number of devices and industrial networks are opened up to public networks. This is beneficial for the administration and organization of the industrial environments. However, it also increases the attack surface, providing possible points of entry for an attacker. Originally, breaking into production networks meant to break an information technology (IT)-perimeter first, such as a public Website, and then to move laterally to industrial control systems (ICSs) to influence the production environment. However, many OT-devices are connected directly to the Internet, which drastically increases the threat of compromise, especially since OT-devices contain several vulnerabilities. In this work, the presence of OT-devices in the Internet is analyzed from an attacker’s perspective. Publicly available tools, such as the search engineShodanand vulnerability databases, are employed to find commonly used OT-devices and map vulnerabilities to them. These findings are grouped according to the country of origin, manufacturer, and number as well as severity of vulnerability. More than 13000 devices were found, almost all contained at least one vulnerability. European and Northern American countries are by far the most affected ones.

Until two decades ago, industrial networks were deemed secure due to physical separation from public networks. An abundance of successful attacks proved that assumption wrong. Intrusion detection solutions for industrial application need to meet certain requirements that differ from home- and office-environments, such as working without feedback to the process and compatibility with legacy systems. Industrial systems are commonly used for several decades, updates are often difficult and expensive. Furthermore, most industrial protocols do not have inherent authentication or encryption mechanisms, allowing for easy lateral movement of an intruder once the perimeter is breached. In this work, an algorithm for motif discovery in time series, Matrix Profiles, is used to detect outliers in the timing behaviour of an industrial process. This process was monitored in an experimental environment, containing ground truth labels after attacks were performed. Furthermore, the graph representations of a different industrial data set that has been emulated are used to detect malicious activities. These activities can be derived from anomalous communication patterns, represented as edges in the graph. Finally, an integration concept for both methods is proposed.

In the context of the Industrial Internet of Things, communication technology, originally used in home and office environments, is introduced into industrial applications. Commercial off-the-shelf products, as well as unified and well-established communication protocols make this technology easy to integrate and use. Furthermore, productivity is increased in comparison to classic industrial control by making systems easier to manage, set up and configure. Unfortunately, most attack surfaces of home and office environments are introduced into industrial applications as well, which usually have very few security mechanisms in place. Over the last years, several technologies tackling that issue have been researched. In this work, machine learning-based anomaly detection algorithms are employed to find malicious traffic in a synthetically generated data set of Modbus/TCP communication of a fictitious industrial scenario. The applied algorithms are Support Vector Machine (SVM), Random Forest, k-nearest neighbour and k-means clustering. Due to the synthetic data set, supervised learning is possible. Support Vector Machine and k-nearest neighbour perform well with different data sets, while k-nearest neighbour and k-means clustering do not perform satisfactorily.