Complementing previous research, this paper addresses the challenge of exploring the drivers of customer dissatisfaction in buyer-manufacturer relationships in a B2B and a business-to-government (B2G) context, to enhance existing models of the outcomes of dissatisfaction. Based on qualitative data in a dyadic research setting, this paper collects and analyzes data from both the buying and manufacturing side in Germany, referring to internationally sold standard and customized product solutions. Customers are not limited to business clients, they include public institutions and partners. Embedding empirical observations and data in established theoretical frames, this paper enhances existing dissatisfaction research by adding its antecedents and suggests a new unified customer satisfaction and dissatisfaction model. Empirical results provide substantial implications for both B2B and B2G marketing management. Additionally, the implications for future academic research are outlined.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F 1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F 1 score of respectively > 99%.

Code similarity measures create a comparison metric showing to what degree two code samples have the same functionality, e.g., to statically detect the use of known libraries in binary code. They are both an indispensable part of automated malware analysis, as well as a helper for the detection of plagiarism (IP protection) and the illegal use of open-source libraries in commercial apps. The centroid similarity metric extracts control-flow features from binary code and encodes them as geometric structures before comparing them. In our paper, we propose novel improvements to the centroid approach and apply it to the ARM architecture for the first time. We implement our approach as a plug-in for the IDA Pro disassembler and evaluate it regarding efficiency, accuracy and robustness on Android. Based on a dataset of 508,745 APKs, collected from 18 third-party app markets, we achieve a detection rate of 89% for the use of native code libraries, with an FPR of 10.8%. To test the robustness of our approach against the compiler version, optimization level, and other code transformations, we obfuscate and recompile known open-source libraries to evaluate which code transformations are resisted. Based on our results, we discuss how code re-use can be hidden by obfuscation and conclude with possible improvements.

With SDN/NFV, the telecom industry embraces operational flexibility and cost optimization, while facing new risks from off-premise cloud computing, known as introspection by malicious operators. Introspection is identified as a serious risk only by the IT industry in general when considering cloud operation. To mitigate it, processor vendors have invested in the last decade to design Trusted Execution Environments (TEEs) plugged into their processor architectures. TEEs bring a quantum hardware-level security higher than any software-based security. They are all essentially aimed at protecting data and code when executed and processed in the cloud or in untrusted environment. In this paper, we emphasize on the blocking factors for the use of TEEs today: processor market fragmentation, major architectural and design deviations between TEEs from various CPU vendors and finally, a relatively complex enablement of these TEE technologies for non-security experts. We describe a code interpretation solution to break those blocking factors by providing a universal abstraction layer for TEEs. The paper gives a conceptual blueprint of a solution that enables Intel's SGX and AMD's SEV, defined as the most contemplated candidates in this paper for SDN/NFV or 5G deployment. Our study presents the key challenges and advanced functionalities we view as essential for meeting key SDN/NFV requirements and which are deploy ability, software performance and easy setup. Innovative directions are given to deal efficiently with these upcoming requirements.

In this paper, we leverage SGX to provide a secure remote computation framework to be used in a cloud scenario. Our framework consists of two parts, a local part running on the user's machine and a remote part which is executed within the provider's environment. Users can connect and authenticate themselves to the remote side, verify the integrity of a newly spawned loading enclave, and deploy confidential code to the provider's machine. While we are not the first using SGX in a cloud scenario, we provide a full implementation considering all practical pitfalls, e.g., we use Intel's Attestation Services to prove the integrity of the loading enclave to our users. We also take care of establishing a secure bidirectional channel between the target enclave and the client running on the user's machine to send code, commands, and data. The performance overhead of CPU-bound applications using our framework is below 10% compared to remote computation without using SGX.

Dynamic analysis of Android malware suffers from techniques that identify the analysis environment and prevent the malicious behavior from being observed. While there are many analysis solutions that can thwart evasive malware on Windows, the application of similar techniques for Android has not been studied in-depth. In this paper, we present Lumus, a novel technique to uncover evasive malware on Android. Lumus compares the execution traces of malware on bare metal and emulated environments. We used Lumus to analyze 1,470 Android malware samples and were able to uncover 192 evasive samples. Comparing our approach with other solutions yields better results in terms of accuracy and false positives. We discuss which information are typically used by evasive malware for detecting emulated environments, and conclude on how analysis sandboxes can be strengthened in the future.

Laser cladding on 200 μm thin substrates is a challenge due to poor heat dissipation. Successful coatings were produced using Yb fiber laser with closed-loop controlled laser power. Samples were investigated by SEM and EDS.

This book describes the results of a research project on compliance and organizational integrity, financed by the German government and conducted over the last three years. The book offers a theoretical framework and valid instruments for measuring the outcome of compliance management: organizational integrity. To pinpoint the specifics of organizational integrity, and to create a framework for assessment, the book analyzes not only the cases of Siemens and Deutsche Bank but also a specific form of organization: governmental organizations. The book includes the results of a survey of employees in five German cities, in the course of which the author conducted interviews with the personnel responsible for compliance in different organizations. In addition, during their discussions he analyzed the administrative staff with regard to the decision-making processes they were involved in.

You all know this from watching CSI: When a crime is committed, usually some form of digital evidence is left on devices such as computers, mobile phones, or the navigation system of a car a suspect has used. Indeed, law enforcement agencies are regularly interested in data from personal devices to find evidence, guide investigations, or even act as proof in a court of law. This tutorial article by Felix Freiling et al. mentions the San Bernadino case as a prominent example. But how do police investigators go about accessing this evidence? Is what is shown on TV realistic? Whereas, in times of classical hard disks, accessing data was quite easy due to the non- volatility of the memory device. However, this is getting increasingly difficult because of developing technologies like SSDs, other forms of flash storage, and, in particular, for volatile memory such as RAM, with the major problem being to read out data while guarding “authenticity.” In the past ten years, there has been some substantial development in the area of forensic data acquisition, which is summarized by the article. It gives clear indications of what currently can be technically done and what cannot be done by police investigators. So, if you watch CSI again and the cops need to access some digital evidence, you can tell truth from fiction. —Jürgen Teich, Friedrich-Alexander-Universität Erlangen-Nürnberg

Due to the requirements of the Internet-of-Things, modern embedded systems have become increasingly complex, running different applications. In order to protect their intellectual property as well as the confidentiality of sensitive data they process, these applications have to be isolated from each other. Traditional memory protection and memory management units provide such isolation, but rely on operating system support for their configuration. However, modern operating systems tend to be vulnerable and cannot guarantee confidentiality when compromised. We present Atlas, a hardware-based security architecture, complementary to traditional memory protection mechanisms, ensuring code and data confidentiality through transparent encryption, even when the system software has been exploited. Atlas relies on its zero-software trusted computing base to protect against system-level attackers and also supports secure shared memory. We implemented Atlas based on the LEON3 softcore processor, including toolchain extensions for developers. Our FPGA-based evaluation shows minimal cycle overhead at the cost of a reduced maximum frequency.

Welcome to the Human Factors in Hypertext 2018 workshop (HUMAN'18) in Baltimore, Maryland, USA, the first workshop of a new series of workshops for the ACM Conference on Hypertext and Social Media. It has a strong focus on hypertext users and thus complements the machine analytics research that we experienced in previous conferences. The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains. Furthermore, the workshop raises the question of how original hypertext ideas (e.g., Doug Engelbart's "augmenting human intellect" or Frank Halasz' "hypertext as a medium for thinking and communication") can improve today's hypertext systems. Historically, hypertext research is strongly connected to human factors. Hypertext pioneers, such as Doug Engelbart or Ted Nelson, focused on the usage of and interaction with hypertext. This workshop combines original hypertext research ideas with recent hypertext research trends. In addition, it consolidates different hypertext research areas from the viewpoint of human factors. Thus, HUMAN'18 fosters cross-cutting discussions and the development of new ideas.

The idea to associate information with so-called links was developed by hypertext pioneers in the 1960s. In the 1990s the Dexter Hypertext Reference Model was developed with the goal to provide a general model for node-link hypertext systems. In the 1990s and 2000s there were important steps made for hypertext infrastructures, which led to component-based open hypermedia systems (CB-OHS). In this paper we provide a detailed description of node-link structures. We argue that Dexter does not match the need of CB-OHS, as it supports a mix of multiple structure domains. Based on the implementation of link support in our system Mother we demonstrate how Dexter needs to be tailored accordingly. We further describe Mother's ability of node-link structures to interoperate with other available structure services and vice versa.

Wie die Blockchain-Technologie die Politik beeinflussen könnte und heute bereits tatsächlich beeinflusst.