TY  - GEN
A1  - Torkura, Kennedy A.
A1  - Sukmana, Muhammad Ihsan Haikal
A1  - Kayem, Anne V. D. M.
A1  - Cheng, Feng
A1  - Meinel, Christoph
T1  - A cyber risk based moving target defense mechanism for microservice architectures
T2  - 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)
N2  - 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.
KW  - Security Risk Assessment
KW  - Security Metrics
KW  - Moving Target Defense
KW  - Microservices Security
KW  - Application Container Security
Y1  - 2018
SN  - 978-1-7281-1141-4
U6  - https://doi.org/10.1109/BDCloud.2018.00137
SN  - 2158-9178
SP  - 932
EP  - 939
PB  - Institute of Electrical and Electronics Engineers
CY  - Los Alamitos
ER  - 
TY  - GEN
A1  - Halfpap, Stefan
A1  - Schlosser, Rainer
T1  - A Comparison of Allocation Algorithms for Partially Replicated Databases
T2  - 2019 IEEE 35th International Conference on Data Engineering (ICDE)
N2  - 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.
Y1  - 2019
SN  - 978-1-5386-7474-1
SN  - 978-1-5386-7475-8
U6  - https://doi.org/10.1109/ICDE.2019.00226
SN  - 1084-4627
SN  - 2375-026X
SN  - 1063-6382
SP  - 2008
EP  - 2011
PB  - IEEE
CY  - New York
ER  - 
TY  - GEN
A1  - Galke, Lukas
A1  - Gerstenkorn, Gunnar
A1  - Scherp, Ansgar
T1  - A case atudy of closed-domain response suggestion with limited training data
T2  - Database and Expert Systems Applications : DEXA 2018 Iinternational workshops
N2  - 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.
Y1  - 2018
SN  - 978-3-319-99133-7
SN  - 978-3-319-99132-0
U6  - https://doi.org/10.1007/978-3-319-99133-7_18
SN  - 1865-0929
SN  - 1865-0937
VL  - 903
SP  - 218
EP  - 229
PB  - Springer
CY  - Berlin
ER  -