Reference models in form of best practices are an essential element to ensured knowledge as design for reuse. Popular modeling approaches do not offer mechanisms to embed reference models in a supporting way, let alone a repository of it. Therefore, it is hardly possible to profit from this expertise. The problem is that the reference models are not described formally enough to be helpful in developing solutions. Consequently, the challenge is about the process, how a user can be supported in designing dedicated solutions assisted by reference models. In this paper, we present a generic approach for the formal description of reference models using semantic technologies and their application. Our modeling assistant allows the construction of solution models using different techniques based on reference building blocks. This environment enables the subsequent verification of the developed designs against the reference models for conformity. Therefore, our reference modeling assistant highlights the interdependency. The application of these techniques contributes to the formalization of requirements and finally to quality assurance in context of maturity model. It is possible to use multiple reference models in context of system of system designs. The approach is evaluated in industrial area and it can be integrated into different modeling landscapes.

Investigating cyber-attacks in a legally binding way becomes more and more difficult, especially through advanced attacks (AT) such as advanced persistent threats (APT) or multihost attacks. Current forensic models do not provide a basis for a process to analyze such attacks. This paper's objective is to find a novel forensic management approach based on agile methods to meet the challenges of ATs. When it comes to the forensic investigation of such attacks, big data problems need to be addressed due to the amount of data that needs to be analyzed. The proposed model meets this requirement by precisely defining the questions that need to be answered in an early state and collecting only the evidence that is needed to answer these questions. Additionally, the novel flower model for AT is presented that meets the different phases of an investigation process.

Software bots have attracted increasing interest and popularity in both research and society. Their contributions span automation, digital twins, game characters with conscious-like behavior, and social media. However, there is still a lack of intelligent bots that can adapt to web environments' variability and dynamic nature. Unlike human users, they have difficulty understanding and exploiting the affordances across multiple virtual environments. Despite the hype, bots with human user-like cognition do not currently exist. Chatbots, for instance, lack situational awareness on the digital platforms where they operate, preventing them from enacting meaningful and autonomous intelligent behavior similar to human users. In this survey, we aim to explore the role of cognitive architectures in supporting efforts towards engineering software bots with advanced general intelligence. We discuss how cognitive architectures can contribute to creating intelligent software bots. Furthermore, we highlight key architectural recommendations for the future development of autonomous, user-like cognitive bots.

The Internet is steadily growing and is of increasing importance for our economy and society. Due to this increased importance it is also in the focus of attacks, e.g. distributed denial of service (DDoS) attacks. As attackers dynamically change their attack behaviour, novel detection approaches that are able to automatically adjust to these dynamic attacks are needed. To train and test such network anomaly detection systems, it is necessary to provide realistic data. As of today, this area of research suffers from the lack of publicly available datasets that can be used to train and test anomaly detection systems and are exchangeable to allow reproducible research. Therefore, we propose a novel framework that enables researchers and developers to generate customizable synthetic datasets. It not only allows to generate fully-synthetic network traffic, but also to generate semi-synthetic network traffic by merging of multiple network captures from reallive environments. Further, it allows the mapping of IP addresses as well as the modi﬿cation of other header ﬿elds, if desired. This enables researchers and developers to exchange network traces from sensitive environments without revealing any sensitive end-user related information, while perceiving the relevant characteristics of the network(s) and attack(s). In the following, we provide a description of, the problem, our concept and the features of our solution, the architecture and functional model and ﬿nally provide a short summary together with an outlook for future work.

The lack of guided exercises and practical opportunities to learn about cybersecurity in a practical way makes it difficult for security experts to improve their proficiency. Capture the Flag events and Cyber Ranges are ideal for cybersecurity training. Thereby, the participants usually compete in teams against each other, or have to defend themselves in a specific scenario. As organizers of yearly events, we present a taxonomy for interactive cyber training and education. The proposed taxonomy includes different factors of the technical setup, audience, training environment, and training setup. By the comprehensive taxonomy, different aspects of interactive training are considered. This can help trainings to improve and to be established successfully. The provided taxonomy is extendable and can be used in further application areas as research on new security technologies.

Software bots operating in multiple virtual digital platforms must understand the platforms' affordances and behave like human users. Platform affordances or features differ from one application platform to another or through a life cycle, requiring such bots to be adaptable. Moreover, bots in such platforms could cooperate with humans or other software agents for work or to learn specific behavior patterns. However, present-day bots, particularly chatbots, other than language processing and prediction, are far from reaching a human user's behavior level within complex business information systems. They lack the cognitive capabilities to sense and act in such virtual environments, rendering their development a challenge to artificial general intelligence research. In this study, we problematize and investigate assumptions in conceptualizing software bot architecture by directing attention to significant architectural research challenges in developing cognitive bots endowed with complex behavior for operation on information systems. As an outlook, we propose alternate architectural assumptions to consider in future bot design and bot development frameworks.

Confidentiality in our digital world is based on the security of cryptographic algorithms. These are usually executed transparently in the background, with people often relying on them without further knowledge. In the course of technological progress with quantum computers, the protective function of common encryption algorithms is threatened. This particularly affects public-key methods such as RSA and DH based on discrete logarithms and prime factorization. Our concept describes the transformation of a classical asymmetric encryption method to a modern complexity class. Thereby the approach of CramerShoup is put on the new basis of elliptic curves. The system is provable cryptographically strong, especially against adaptive chosen-ciphertext attacks. In addition, the new method features small key lengths, making it suitable for Internet-of-Things. It represents an intermediate step towards an encryption scheme based on isogeny elliptic curves. This approach shows a way to a secure encryption scheme for the post-quantum computing era.

In this paper, we propose a method for aligning models with their realization through the application of model-based systems engineering. Our approach is divided into three steps. (1) Firstly, we leverage domain expertise and the Unified Architecture Framework to establish a reference model that fundamentally describes some domain. (2) Subsequently, we instantiate the reference model as specific models tailored to different scenarios within the domain. (3) Finally, we incorporate corresponding run logic directly into both the reference model and the specific models. In total, we thus provide a practical means to ensure that every implementation result is justified by business demand. We demonstrate our approach using the example of maritime object detection as a specific application (specific model/implementation element) of automatic target recognition as a service reoccurring in various forms (reference model element). Our approach facilitates a more seamless integration of models and implementation, fostering enhanced Business-IT alignment.

The first step towards digitalization within organizations lies in digitization - the conversion of analog data into digitally stored data. This basic step is the prerequisite for all following activities like the digitalization of processes or the servitization of products or offerings. However, digitization itself often leads to “data-rich” but “knowledge-poor” material. Knowledge discovery and knowledge extraction as approaches try to increase the usefulness of digitized data. In this paper, we point out the key challenges in the context of knowledge discovery and present an approach to addressing these using a microservices architecture. Our solution led to a conceptual design focusing on keyword extraction, similarity calculation of documents, database queries in natural language, and programming language independent provision of the extracted information. In addition, the conceptual design provides referential design guidelines for integrating processes and applications for semi-automatic learning, editing, and visualization of ontologies. The concept also uses a microservices architecture to address non-functional requirements, such as scalability and resilience. The evaluation of the specified requirements is performed using a demonstrator that implements the concept. Furthermore, this modern approach is used in the German patent office in an extended version.

According to experts, one third of all IT vulnerabilities today are due to inadequate software verification. Internal program processes are not sufficiently secured against manipulation by attackers, especially if access has been gained. There is a lack of internal control instances that can monitor and control program flows. Especially when a software vulnerability becomes known, quick action is required, whereby the consequences for an individual application are often not foreseeable. With our approach (B)LOCKBOX, software building blocks act as verified entities within a transaction-based blockchain network. Source Code, binaries and application execution become supervised. Unwanted interference and manipulation are prevented by the integrity of the distributed system.

The knowledge of the world is passed on through libraries. Accordingly, domain expertise and experiences should also be transferred within an enterprise by a knowledge base. Therefore, models are an established medium to describe good practices for complex systems, processes, and interconnections. However, there is no structured and detailed approach for a design of an enterprise model library. The objective of this work is the reference architecture of a repository for models with function of reuse. It includes the design of the data structure for filing, the processes for administration and possibilities for usage. Our approach enables consistent mapping of requirements into models via meta-data attributes. Furthermore, the adaptation of reference architectures in specific use cases as well as a reconciliation of interrelationships is enabled. A case study with industry demonstrates the practical benefits of reusing work already done. It provides an organization with systematic access to specifications, standards and guidelines. Thus, further development is accelerated and supported in a structured manner, while complexity remains controllable. The presented approach enriches various enterprise architecture frameworks. It provides benefits for development based on models.

Models in face of increasing complexity support development of new systems and enterprises. For an efficient procedure, reference models are adapted in order to reach a solution with les overhead which covers all necessary aspects. Here, a key challenge is applying a consistent methodology for the descriptions of such reference designs. This paper presents a holistic approach to describe reference models across different views and levels. Modeling stretches from the requirements and capabilities over their subdivision to services and components up to the realization in processes and data structures. Benefits include an end-to-end traceability of the capability coverage with performance parameters considered already at the starting point of the reference design. This enables focused development while considering design constraints and potential bottlenecks. We demonstrate the approach on the example of the development of a smart robot. Here, our methodology highly supports transferability of designs for the development of further systems.

Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the Enterprise Architecture Framework comes in. It empowers an organization to get a clear view of their business, application, technical and physical layer. This modeling approach is an established method for organizations to take a deeper look into their structure and processes. The development of such models requires a great deal of effort, is carried out manually by interviewing stakeholders and requires continuous maintenance. Our new approach enables the automated mining of Enterprise Architecture models. The system uses common technologies to collect the metadata based on network traffic, log files and other information in an organization. Based on this, the new approach generates EA models with the desired views points. Furthermore, a rule and knowledge-based reasoning is used to obtain a holistic overview. This offers a strategic decision support from business structure over process design up to planning the appropriate support technology. Therefore, it forms the base for organizations to act in an agile way. The modeling can be performed in different modeling languages, including ArchiMate and the Nato Architecture Framework (NAF). The designed approach is already evaluated on a small company with multiple services and an infrastructure with several nodes.

With increasing linkage within value chains, the IT systems of different companies are also being connected with each other. This enables the integration of services within the movement of Industry 4.0 in order to improve the quality and performance of the processes. Enterprise architecture models form the basis for this with a better buisness IT-alignment. However, the heterogeneity of the modeling frameworks and description languages makes a concatenation considerably difficult, especially differences in syntax, semantic and relations. Therefore, this paper presents a transformation engine to convert enterprise architecture models between several languages. We developed the first generic translation approach that is free of specific meta-modeling, which is flexible adaptable to arbitrary modeling languages. The transformation process is defined by various pattern matching techniques using a rule-based description language. It uses set theory and first-order logic for an intuitive description as a basis. The concept is practical evaluated using an example in the area of a large German IT-service provider. Anyhow, the approach is applicable between a wide range of enterprise architecture frameworks.

The constantly growing size of blockchains becomes a challenge with the increasing usage. Especially the storage of unwanted data in a blockchain is an issue, because it cannot be removed naturally. In order to counteract this problem, we present the first concept for the selective deletion of single entries in a blockchain. For this purpose, the general consensus algorithm is extended by the functionality of regularly creating summary blocks. Previous data of the chain are summarized and stored again in a new block, leaving out unwanted information. With a shifting marker of the Genesis Block, data can be deleted from the beginning of a blockchain. In this way, the technology of the blockchain becomes fully transactional. The concept is independent of a specific block structure, network structure, or consensus algorithm. Moreover, this functionality can be adapted to current blockchains to solve multiple problems related to scalability. This approach enables the transfer of blockchain technology to further fields of application, among others in the area of Industry 4.0 and Product Life-cycle Management.

With rapid increase of mobile computing and wireless network linkage, the information exchange between connected systems and within groups increases heavily. Exchanging confidential information within groups via unsecured communication channels is a high security threat. In order to prevent third parties from accessing this data, it is essential to encrypt it. For this purpose, the group participants need a common group key to enable encrypted broadcast messages. But efficient key management of secured group communication is a challenging task, if participants rely on low performance hardware and small bandwidth. For coordination and distribution, we present the modular group key management procedure CAKE that is centrally organized and meets strict security requirements. The lightweight G-IKEv2 protocol in combination with the key exchange concept of CAKE leads to an efficiently integrated solution. The hybrid approach combines the advantages of the existing protocols with the objective to reduce the computation and communication effort. It is shown that the procedure is more suitable for changing MANET groups than the existing ones. Moreover, the exchanged group key can be used for any services which provides a wide range of applications.

IP Geolocation is a key enabler for the Future Internet to provide geographical location information for application services.For example, this data is used by Content Delivery Networks to assign users to mirror servers, which are close by, hence providing enhanced traffic management.It is still a challenging task to obtain precise and stable location information, whereas proper results are only achieved by the use of active latency measurements.This paper presents an advanced approach for an accurate and self-optimizing model for location determination, including identification of optimized Landmark positions, which are used for probing.Moreover, the selection of correlated data and the estimated target location requires a sophisticated strategy to identify the correct position.We present an improved approximation of network distances of usually unknown TIER infrastructures using the road network.Our concept is evaluated under real-world conditions focusing Europe.

The forensic investigation of cyber attacks and IT incidents is becoming increasingly difficult due to increasing complexity and intensify networking. Especially with Advanced Attacks (AT) like the increasing Advanced Persistent Threats an agile approach is indispensable. Several systems are involved in an attack (multi-host attacks). Current forensic models and procedures show considerable deficits in the process of analyzing such attacks. For this purpose, this paper presents the novel flower model, which uses agile methods and forms a new forensic management approach. In this way, the growing challenges of ATs are met. In the forensic investigation of such attacks, big data problems have to be solved due to the amount of data that needs to be analyzed. The proposed model meets this requirement by precisely defining the questions that need to be answered in an early state and collecting only the evidence usable in court proceedings that is needed to answer these questions. Additionally, the novel flower model for AT is presented that meets the different phases of an investigation process.

Cyber incidents can have a wide range of cause from a simple connection loss to an insistent attack. Once a potential cyber security incidents and system failures have been identified, deciding how to proceed is often complex. Especially, if the real cause is not directly in detail determinable. Therefore, we developed the concept of a Cyber Incident Handling Support System. The developed system is enriched with information by multiple sources such as intrusion detection systems and monitoring tools. It uses over twenty key attributes like sync-package ratio to identify potential security incidents and to classify the data into different priority categories. Afterwards, the system uses artificial intelligence to support the further decision-making process and to generate corresponding reports to brief the Board of Directors. Originating from this information, appropriate and detailed suggestions are made regarding the causes and troubleshooting measures. Feedback from users regarding the problem solutions are included into future decision-making by using labelled flow data as input for the learning process. The prototype shows that the decision making can be sustainably improved and the Cyber Incident Handling process becomes much more effective.