Simon D. Duque Antón

Simon Daniel Duque Anton, Daniel Schneider, Hans Dieter Schotten

Artificial Intelligence (AI) increasingly shows its potential to outperform predicate logic algorithms and human control alike. In automatically deriving a system model, AI algorithms learn relations in data that are not detectable for humans. This great strength, however, also makes use of AI methods dubious. The more complex a model, the more difficult it is for a human to understand the reasoning for the decisions. As currently, fully automated AI algorithms are sparse, every algorithm has to provide a reasoning for human operators. For data engineers, metrics such as accuracy and sensitivity are sufficient. However, if models are interacting with non-experts, explanations have to be understandable. This work provides an extensive survey of literature on this topic, which, to a large part, consists of other surveys. The findings are mapped to ways of explaining decisions and reasons for explaining decisions. It shows that the heterogeneity of reasons and methods of and for explainability lead to individual explanatory frameworks.

Anna-Pia Lohfink, Simon D. Duque Anton, Heike Leitte et al.

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an application-agnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities.

Daniel Reti, David Klaaßen, Simon Duque Anton et al.

Deceiving an attacker in the network security domain is a well established approach, mainly achieved through deployment of honeypots consisting of open network ports with the sole purpose of raising an alert on a connection. With attackers becoming more careful to avoid honeypots, other decoy elements on real host systems continue to create uncertainty for attackers. This uncertainty makes an attack more difficult, as an attacker cannot be sure whether the system does contain deceptive elements or not. Consequently, each action of an attacker could lead to the discovery. In this paper a framework is proposed for placing decoy elements through an SSH proxy, allowing to deploy decoy elements on-the-fly without the need for a modification of the protected host system.

Simon D Duque Anton, Daniel Fraunholz, Daniel Schneider

The internet landscape is growing and at the same time becoming more heterogeneous. Services are performed via computers and networks, critical data is stored digitally. This enables freedom for the user, and flexibility for operators. Data is easier to manage and distribute. However, every device connected to a network is potentially susceptible to cyber attacks. Security solutions, such as antivirus software or firewalls, are widely established. However, certain types of attacks cannot be prevented with defensive measures alone. Offensive security describes the practice of security professionals using methods and tools of cyber criminals. This allows them to find vulnerabilities before they become the point of entry in a real attack. Furthermore, following the methods of cyber criminals enables security professionals to adapt to a criminal's point of view and potentially discover attack angles formerly ignored. As cyber criminals often employ freely available security tools, having knowledge about these provides additional insight for professionals. This work categorises and compares tools regarding metrics concerning maintainability, usability and technical details. Generally, several well-established tools are available for the first phases, while phases after the initial breach lack a variety of tools.

Simon D Duque Anton, Hans Dieter Schotten

The digitisation of industry provides a plethora of novel applications that increase flexibility and reduce setup and maintenance time as well as cost. Furthermore, novel use cases are created by the digitisation of industry, commonly known as Industry 4.0 or the Industrial Internet of Things, applications make use of communication and computation technology that is becoming available. This enables novel business use cases, such as the digital twin, customer individual production, and data market places. However, the inter-connectivity such use cases rely on also significantly increases the attack surface of industrial enterprises. Sabotage and espionage are aimed at data, which is becoming the most crucial asset of an enterprise. Since the requirements on security solutions in industrial networks are inherently different from office networks, novel approaches for intrusion detection need to be developed. In this work, process data of a real water treatment process that contains attacks is analysed. Analysis is performed by an extension of Matrix Profiles, a motif discovery algorithm for time series. By extending Matrix Profiles with a Hammingdistance metric, binary and tertiary actuators can be integrated into the analysis in a meaningful fashion. This algorithm requires low training effort while providing accurate results. Furthermore, it can be employed in a real-time fashion. Selected actuators in the data set are analysed to highlight the applicability of the extended Matrix Profiles.

Anna-Pia Lohfink, Simon D. Duque Anton, Hans Dieter Schotten et al.

Operation technology networks, i.e. hard- and software used for monitoring and controlling physical/industrial processes, have been considered immune to cyber attacks for a long time. A recent increase of attacks in these networks proves this assumption wrong. Several technical constraints lead to approaches to detect attacks on industrial processes using available sensor data. This setting differs fundamentally from anomaly detection in IT-network traffic and requires new visualization approaches adapted to the common periodical behavior in OT-network data. We present a tailored visualization system that utilizes inherent features of measurements from industrial processes to full capacity to provide insight into the data and support triage analysis by laymen and experts. The novel combination of spiral plots with results from anomaly detection was implemented in an interactive system. The capabilities of our system are demonstrated using sensor and actuator data from a real-world water treatment process with introduced attacks. Exemplary analysis strategies are presented. Finally, we evaluate effectiveness and usability of our system and perform an expert evaluation.

Simon D. Duque Anton, Anna Pia Lohfink, Hans Dieter Schotten

The fourth industrial revolution leads to an increased use of embedded computation and intercommunication in an industrial environment. While reducing cost and effort for set up, operation and maintenance, and increasing the time to operation or market respectively as well as the efficiency, this also increases the attack surface of enterprises. Industrial enterprises have become targets of cyber criminals in the last decade, reasons being espionage but also politically motivated. Infamous attack campaigns as well as easily available malware that hits industry in an unprepared state create a large threat landscape. As industrial systems often operate for many decades and are difficult or impossible to upgrade in terms of security, legacy-compatible industrial security solutions are necessary in order to create a security parameter. One plausible approach in industry is the implementation and employment of side-channel sensors. Combining readily available sensor data from different sources via different channels can provide an enhanced insight about the security state. In this work, a data set of an experimental industrial set up containing side channel sensors is discussed conceptually and insights are derived.

Simon D. Duque Anton, Anna Pia Lohfink, Christoph Garth et al.

Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort. Furthermore, the fourth industrial revolution, creating the so-called Industry 4.0, opens a variety of novel use and business cases in the industrial environment. However, this progress comes at the cost of an enlarged attack surface of industrial companies. Operational networks that have previously been phyiscally separated from public networks are now connected in order to make use of new communication capabilites. This motivates the need for industrial intrusion detection solutions that are compatible to the long-term operation machines in industry as well as the heterogeneous and fast-changing networks. In this work, process data is analysed. The data is created and monitored on real-world hardware. After a set up phase, attacks are introduced into the systems that influence the process behaviour. A time series-based anomaly detection approach, the Matrix Profiles, are adapted to the specific needs and applied to the intrusion detection. The results indicate an applicability of these methods to detect attacks in the process behaviour. Furthermore, they are easily integrated into existing process environments. Additionally, one-class classifiers One-Class Support Vector Machines and Isolation Forest are applied to the data without a notion of timing. While Matrix Profiles perform well in terms of creating and visualising results, the one-class classifiers perform poorly.

Simon D. Duque Anton, Sapna Sinha, Hans Dieter Schotten

Attacks on industrial enterprises are increasing in number as well as in effect. Since the introduction of industrial control systems in the 1970's, industrial networks have been the target of malicious actors. More recently, the political and warfare-aspects of attacks on industrial and critical infrastructure are becoming more relevant. In contrast to classic home and office IT systems, industrial IT, so-called OT systems, have an effect on the physical world. Furthermore, industrial devices have long operation times, sometimes several decades. Updates and fixes are tedious and often not possible. The threats on industry with the legacy requirements of industrial environments creates the need for efficient intrusion detection that can be integrated into existing systems. In this work, the network data containing industrial operation is analysed with machine learning- and time series- based anomaly detection algorithms in order to discover the attacks introduced to the data. Two different data sets are used, one Modbus-based gas pipeline control traffic and one OPC UA-based batch processing traffic. In order to detect attacks, two machine learning-based algorithms are used, namely \textit{SVM} and Random Forest. Both perform well, with Random Forest slightly outperforming SVM. Furthermore, extracting and selecting features as well as handling missing data is addressed in this work.

Simon Duque Anton, Daniel Fraunholz, Janis Zemitis et al.

Interconnectivity of production machines is a key feature of the Industrial Internet of Things (IIoT). This feature allows for many advantages in producing. Configuration and maintenance gets easier, as access to the given production unit is not necessarily coupled to physical presence. Customized production of goods is easily possible, reducing production times and increasing throughput. There are, however, also dangers to the increasing talkativeness of industrial production machines. The more open a system is, the more points of entry for an attacker exist. Furthermore, the amount of data a production site also increases rapidly due to the integrated intelligence and interconnectivity. To keep track of this data in order to detect attacks and errors in the production site, it is necessary to smartly aggregate and evaluate the data. In this paper, we present a new approach for collecting, aggregating and analysing data from different sources and on three different levels of abstraction. Our model is event-centric, considering every occurrence of information inside the system as an event. In the lowest level of abstraction, singular packets are collected, correlated with log-entries and analysed. On the highest level of abstraction, networks are pictured as a connectivity graph, enriched with information about host-based activities. Furthermore, we describe our work in progress of evaluating our aggregation model on two different system settings. In the first scenario, we verify the usability of our model in a remote maintenance application. In the second scenario, we evaluate our model in the context of network sniffing and correlation with log-files. First results show that our model is a promising solution to cope with increasing amounts of data and to correlate information from different types of sources.

Simon D. Duque Antón, Hans Dieter Schotten

Besides the advantages derived from the ever present communication properties, it increases the attack surface of a network as well. As industrial protocols and systems were not designed with security in mind, spectacular attacks on industrial systems occurred over the last years. Most industrial communication protocols do not provide means to ensure authentication or encryption. This means attackers with access to a network can read and write information. Originally not meant to be connected to public networks, the use cases of Industry 4.0 require interconnectivity, often through insecure public networks. This lead to an increasing interest in information security products for industrial applications. In this work, the concept for holistic intrusion detection methods in an industrial context is presented. It is based on different works considering several aspects of industrial environments and their capabilities to identify intrusions as an anomaly in network or process data. These capabilities are based on preceding experiments on real and synthetic data. In order to justify the concept, an overview of potential and actual attack vectors and attacks on industrial systems is provided. It is shown that different aspects of industrial facilities, e.g. office IT, shop floor OT, firewalled connections to customers and partners are analysed as well as the different layers of the automation pyramid require different methods to detect attacks. Additionally, the singular steps of an attack on industrial applications are characterised. Finally, a resulting concept for integration of these methods is proposed, providing the means to detect the different stages of an attack by different means.

Simon Duque Antón, Michael Gundall, Daniel Fraunholz et al.

There are hardly any data sets publicly available that can be used to evaluate intrusion detection algorithms. The biggest threat for industrial applications arises from state-sponsored and criminal groups. Often, formerly unknown exploits are employed by these attackers, so-called 0-day exploits. They cannot be discovered with signature-based intrusion detection. Thus, statistical or machine learning based anomaly detection lends itself readily. These methods especially, however, need a large amount of labelled training data. In this work, an exemplary industrial use case with real-world industrial hardware is presented. Siemens S7 Programmable Logic Controllers are used to control a real world-based control application using the OPC UA protocol: A pump, filling and emptying water tanks. This scenario is used to generate application specific network data. Furthermore, attacks are introduced into this data set. This is done in three ways: First, the normal process is monitored and captured. Common attacks are then synthetically introduced into this data set. Second, malicious behaviour is implemented on the Programmable Logic Controller program and executed live, the traffic is captured as well. Third, malicious behaviour is implemented on the Programmable Logic Controller while still keeping the same output behaviour as in normal operation. An attacker could exploit an application but forge valid sensor output so that no anomaly is detected. Sensors are employed, capturing temperature, sound and flow of water to create data that can be correlated to the network data and used to still detect the attack. All data is labelled, containing the ground truth, meaning all attacks are known and no unknown attacks occur. This makes them perfect for training of anomaly detection algorithms. The data is published to enable security researchers to evaluate intrusion detection solutions.

Simon D. Duque Anton, Alexander Hafner, Hans Dieter Schotten

In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.

Simon D. Duque Anton, Mathias Strufe, Hans Dieter Schotten

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.

Simon Duque Anton, Lia Ahrens, Daniel Fraunholz et al.

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.

Daniel Fraunholz, Simon Duque Anton, Christoph Lipps et al.

Deception boosts security for systems and components by denial, deceit, misinformation, camouflage and obfuscation. In this work an extensive overview of the deception technology environment is presented. Taxonomies, theoretical backgrounds, psychological aspects as well as concepts, implementations, legal aspects and ethics are discussed and compared.

Simon Duque Anton, Daniel Fraunholz, Hans Dieter Schotten

The increasing interconnectivity of industrial networks is one of the central current hot topics. It is adressed by research institutes, as well as industry. In order to perform the fourth industrial revolution, a full connectivity between production facilities is necessary. Due to this connectivity, however, an abundance of new attack vectors emerges. In the National Reference Project for Industrial IT-Security (IUNO), these risks and threats are addressed and solutions are developed. These solutions are especially applicable for small and medium sized enterprises that have not as much means in staff as well as money as larger companies. These enterprises should be able to implement the solutions without much effort. The security solutions are derived from four use cases and implemented prototypically. A further topic of this work are the research areas of the German Research Center for Artificial Intelligence that address the given challenges, as well as the solutions developed in the context of IUNO. Aside from the project itself, a method for distributed network data collection aggregation is presented, as a prerequisite for anomaly detection for network security.