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XML Encryption and XML Signature are fundamental security standards forming the core for many applications which require to process XML-based data. Due to the increased usage of XML in distributed systems and platforms such as in SOA and Cloud settings, the demand for robust and effective security mechanisms increased as well. Recent research work discovered, however, substantial vulnerabilities in these standards as well as in the vast majority of the available implementations. Amongst them, the so-called XML Signature Wrapping attack belongs to the most relevant ones. With the many possible instances of this attack type, it is feasible to annul security systems relying on XML Signature and to gain access to protected resources as has been successfully demonstrated lately for various Cloud infrastructures and services. This paper contributes a comprehensive approach to robust and effective XML Signatures for SOAP-based Web Services. An architecture is proposed, which integrates the r equired enhancements to ensure a fail-safe and robust signature generation and verification. Following this architecture, a hardened XML Signature library has been implemented. The obtained evaluation results show that the developed concept and library provide the targeted robustness against all kinds of known XML Signature Wrapping attacks. Furthermore the empirical results underline, that these security merits are obtained at low efficiency and performance costs as well as remain compliant with the underlying standards.
The documentation requirements of data published in long term archives have significantly grown over the last decade. At WDCC the data publishing process is assisted by “Atarrabi”, a web-based workflow system for reviewing and editing metadata information by the data authors and the publication agent. The system ensures high metadata quality for long-term use of the data with persistent identifiers (DOI/URN). By these well-defined references (DOI) credit can properly be given to the data producers in any publication.
In this paper we summarize our research on international educational contexts and transfer the results to the context of urban life-long learning. We will show that a collection and provision of relevant data can help instructors as well as learners to raise their awareness regarding contextual differences, to develop a higher level of acceptance regarding differences, and thus, in the long term, avoid frustration in educational processes and reduce drop out-rates.
In the context of Internet-based e-Learning, including an international auditory is a logical consequence. However, due to uncertainty regarding the foreign learners, e-Learning programs often are limited to local or national participants. Understanding the different expectations of learners regarding instructor-support is one step in order to enable providers of educational services to tailor educational programs that fit the requirements of an international auditory. We asked university students in five countries regarding their expectations towards instructor-support and found major differences between the investigated countries.
For learners, feedback can be both, a strong motivator but in case it fails its purpose, it can be a strong reason for frustration and dropouts as well. Do we have to change our locally implemented feedback strategies when adapting learning contents from national to international settings? In our study, we the investigated learners’ understanding and preferences regarding feedback in scenarios of higher education across the five different national contexts, Bulgaria, Germany, South Korea, Turkey, and Ukraine.
This presentation shows that students in different cultural contexts have different perceptions of time management and work organization. Particularly in group work scenarios, such differences can have a frustrating impact on students from other cultural contexts because e.g., expectations are not met. Being aware of such differences between the learners in a culturally heterogeneous educational scenario, educators can prevent frustration by introducing their students and providing more specific instructions.
Approximate clone detection is the process of identifying similar process fragments in business process model collections. The tool presented in this paper can efficiently cluster approximate clones in large process model repositories. Once a repository is clustered, users can filter and browse the clusters using different filtering parameters. Our tool can also visualize clusters in the 2D space, allowing a better understanding of clusters and their member fragments. This demonstration will be useful for researchers and practitioners working on large process model repositories, where process standardization is a critical task for increasing the consistency and reducing the complexity of the repository.