@misc{TorkuraSukmanaKayemetal.2018,
  author    = {Torkura, Kennedy A. and Sukmana, Muhammad Ihsan Haikal and Kayem, Anne V. D. M. and Cheng, Feng and Meinel, Christoph},
  title     = {A cyber risk based moving target defense mechanism for microservice architectures},
  series = {IEEE Intl Conf on Parallel \& Distributed Processing with Applications, Ubiquitous Computing \& Communications, Big Data \& Cloud Computing, Social Computing \& Networking, Sustainable Computing \& Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)},
  journal   = {IEEE Intl Conf on Parallel \& Distributed Processing with Applications, Ubiquitous Computing \& Communications, Big Data \& Cloud Computing, Social Computing \& Networking, Sustainable Computing \& Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {Los Alamitos},
  isbn      = {978-1-7281-1141-4},
  issn      = {2158-9178},
  doi       = {10.1109/BDCloud.2018.00137},
  pages     = {932 -- 939},
  year      = {2018},
  abstract  = {Microservice Architectures (MSA) structure applications as a collection of loosely coupled services that implement business capabilities. The key advantages of MSA include inherent support for continuous deployment of large complex applications, agility and enhanced productivity. However, studies indicate that most MSA are homogeneous, and introduce shared vulnerabilites, thus vulnerable to multi-step attacks, which are economics-of-scale incentives to attackers. In this paper, we address the issue of shared vulnerabilities in microservices with a novel solution based on the concept of Moving Target Defenses (MTD). Our mechanism works by performing risk analysis against microservices to detect and prioritize vulnerabilities. Thereafter, security risk-oriented software diversification is employed, guided by a defined diversification index. The diversification is performed at runtime, leveraging both model and template based automatic code generation techniques to automatically transform programming languages and container images of the microservices. Consequently, the microservices attack surfaces are altered thereby introducing uncertainty for attackers while reducing the attackability of the microservices. Our experiments demonstrate the efficiency of our solution, with an average success rate of over 70\% attack surface randomization.},
  language  = {en}
}
@misc{HalfpapSchlosser2019,
  author    = {Halfpap, Stefan and Schlosser, Rainer},
  title     = {A Comparison of Allocation Algorithms for Partially Replicated Databases},
  series = {2019 IEEE 35th International Conference on Data Engineering (ICDE)},
  journal   = {2019 IEEE 35th International Conference on Data Engineering (ICDE)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-7474-1},
  issn      = {1084-4627},
  doi       = {10.1109/ICDE.2019.00226},
  pages     = {2008 -- 2011},
  year      = {2019},
  abstract  = {Increasing demand for analytical processing capabilities can be managed by replication approaches. However, to evenly balance the replicas' workload shares while at the same time minimizing the data replication factor is a highly challenging allocation problem. As optimal solutions are only applicable for small problem instances, effective heuristics are indispensable. In this paper, we test and compare state-of-the-art allocation algorithms for partial replication. By visualizing and exploring their (heuristic) solutions for different benchmark workloads, we are able to derive structural insights and to detect an algorithm's strengths as well as its potential for improvement. Further, our application enables end-to-end evaluations of different allocations to verify their theoretical performance.},
  language  = {en}
}
@misc{GalkeGerstenkornScherp2018,
  author    = {Galke, Lukas and Gerstenkorn, Gunnar and Scherp, Ansgar},
  title     = {A case atudy of closed-domain response suggestion with limited training data},
  series = {Database and Expert Systems Applications : DEXA 2018 Iinternational workshops},
  volume    = {903},
  journal   = {Database and Expert Systems Applications : DEXA 2018 Iinternational workshops},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-319-99133-7},
  issn      = {1865-0929},
  doi       = {10.1007/978-3-319-99133-7_18},
  pages     = {218 -- 229},
  year      = {2018},
  abstract  = {We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.},
  language  = {en}
}