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IDEA - An information superstructure 2

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IDEA - An information superstructure

2nd DARPA Information Survivability Conference and Exposition (DISCEX II)

作者： Tallet, JO Henning, RR Fox, KL IDEA Project United States Harris' Software Architecture Tools Methods and Technology Team United States Harris Corporation's Government Communications Systems Division United States Information Assurance Center of Excellence United States Harris Engineering Process Group United States IEEE IEEE Computer Society United States

ISBN: (纸本)0769512127

This past October, the Associated Press reported that for over a month hackers effectively penetrated Microsoft's firewall and, potentially, had access to source code for future products. Over the past year, reported commercial espionage has been increasing steadily. These events underscore the necessary for security engineers to define and implement strong information assurance infrastructures. The concept of defense in depth is used to provide sufficient detection and defensive countermeasures to ensure an enterprise's information is protected. Over the past three years, Harris has conducted research leading to the development of an assurance superstructure tool: the Integrated Design Environment for Assurance (IDEA). Building upon concepts developed during the Network Vulnerability Tool (NVT) program, the IDEA program refined the design and provides a comprehensive assurance analysis superstructure. The IDEA program benifited from the NVT architecture to gain greater adaptive capabilities to address the evolving capabilities of external vulnerability assessment and risk management tools. This re-architected solution builds upon lessons learned during the NVT project.

关键词： architecture patterns FuzzyFusion(Tm) data fusion fuzzy information operations information security

Asset logging in the energy sector: a scalable blockchain-based data platform

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Energy Informatics 2021年第3期4卷 1-20页

作者： Djamali, Alexander Dossow, Patrick Hinterstocker, Michael Schellinger, Benjamin Sedlmeir, Johannes Völter, Fabiane Willburger, Lukas FfE Am Blütenanger 71 München80995 Germany Project Group Business & Information Systems Engineering of the Fraunhofer FIT Wittelsbacherring 10 Bayreuth95444 Germany Research Center Finance & Information Management Universitätsstraße 12 Augsburg86159 Germany University of Bayreuth Universitätsstraße 30 Bayreuth95447 Germany University of Augsburg Universitätsstraße 2 Augsburg86159 Germany

Due to a steeply growing number of energy assets, the increasingly decentralized and segmented energy sector fuels the potential for new digital use cases. In this paper, we focus our attention on the application field of asset logging, which addresses the collection, documentation, and usage of relevant asset data for direct or later verification. We identified a number of promising use cases that so far have not been implemented;supposedly due to the lack of a suitable technical infrastructure. Besides the high degree of complexity associated with various stakeholders and the diversity of assets involved, the main challenge we found in asset logging use cases is to guarantee the tamper-resistance and integrity of the stored data while meeting scalability, addressing cost requirements, and protecting sensitive data. Against this backdrop, we present a blockchain-based platform and argue that it can meet all identified requirements. Our proposed technical solution hierarchically aggregates data in Merkle trees and leverages Merkle proofs for the efficient and privacy-preserving verification of data integrity, thereby ensuring scalability even for highly frequent data logging. By connecting all stakeholders and assets involved on the platform through bilateral and authenticated communication channels and adding a blockchain as a shared foundation of trust, we implement a wide range of asset logging use cases and provide the basis for leveraging platform effects in future use cases that build on verifiable data. Along with the technical aspects of our solution, we discuss the challenges of its practical implementation in the energy sector and the next steps for testing in a regulatory sandbox approach. © 2021, The Author(s).

关键词： Blockchain

An ontology-based approach to analyzing the occurrence of code smells in software 19

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An ontology-based approach to analyzing the occurrence of co...

19th International Conference on Enterprise Information systems, ICEIS 2017

作者： Da Silva Carvalho, Luis Paulo Novais, Renato Do Nascimento Salvador, Laís De Mendonça Neto, Manoel Gomes Federal Institute of Bahia Av. Amazonas 3150 Zabelê Vitória da Conquista Brazil Federal Institute of Bahia Rua Emídio dos Santos s/n Salvador Brazil S/n - Campus de Ondina Federal University of Bahia Av. Ademar de Barros Salvador Brazil Fraunhofer Project Center for Software and Systems Engineering at UFBA Av. Paralela Salvador Brazil

ISBN: (纸本)9789897582486

Code Smells indicate potential flaws in software design that can lead to costly consequences. To mitigate the bad effects of Code Smells, it is necessary to detect and fix defective code. Programmatic processing of Code Smells is not new. Previous works have focused on detection and representation to support the analysis of faulty software. However, such works are based on a syntactic operation, without taking advantage on semantic properties of the software. On the other hand, there are several ways to provide semantic support in software development as a whole. Ontologies, for example, have recently been usedl. The application of ontologies for inferring semantic mechanisms to aid software engineers in dealing with smells may be of great value. As little attention has been given to this, we propose an ontology-based approach to analyze the occurrence of Code Smells in software projects. First, we present a comprehensive ontology that is capable of representing Code Smells and their association with software projects. We also introduce a tool that can manipulate our ontology in order to provide processing of Code Smells as it mines software source-code. Finally, we conducted an initial evaluation of our approach in a real usage scenario with two large open-source software repositories. ©2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Ontology

Domain-specific tailoring of code smells: an empirical study

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Domain-specific tailoring of code smells: an empirical study

International Conference on software engineering (ICSE)

作者： Yuepu Guo Carolyn Seaman Nico Zazworka Forrest Shull Department of Information Systems University of Maryland Baltimore County Baltimore MD USA Fraunhofer Center of Experimental Software Engineering College Park MD USA

Code smells refer to commonly occurring patterns in source code that indicate poor programming practices or code decay. Detecting code smells helps developers find design problems that can cause trouble in future maintenance. Detection rules for code smells, based on software metrics, have been proposed, but they do not take domain-specific characteristics into consideration. In this study we investigate whether such generic heuristics can be tailored to include domain-specific factors. Input into these domain-specific heuristics comes from an iterative empirical field study in a software maintenance project. The results yield valuable insight into code smell detection.

关键词： software Measurement Programming Graphical user interfaces Encoding Couplings Semantics

Supporting defect causal analysis in practice with cross-company data on causes of requirements engineering problems

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arXiv 2017年

作者： Kalinowski, Marcos Curty, Pablo Paes, Aline Spínola, Rodrigo Felderer, Michael Ferreira, Alexandre Fernández, Daniel Méndez Wagner, Stefan Niterói Brazil Fraunhofer Project Center at UFBA Salvador Brazil Institute of Computer Science University of Innsbruck Innsbruck Austria Rio de Janeiro Brazil Software & Systems Engineering Technical University of Munich Munich Germany Institute of Software Technology University of Stuttgart Stuttgart Germany

[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the fraunhofer project center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated. Copyright © 2017, The Authors. All rights reserved.

关键词： Requirements engineering

Shedding Light on Blind Spots - Developing a Reference Architecture to Leverage Video Data for Process Mining

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arXiv 2020年

作者： Kratsch, Wolfgang K¨nig, Fabian R¨glinger, Maximilian Fim Research Center University of Applied Sciences Augsburg Germany Fim Research Center University of Bayreuth Germany Project Group Business & Information Systems Engineering of the Fraunhofer Fit Germany

Process mining is one of the most active research streams in business process management. In recent years, numerous methods have been proposed for analyzing structured process data. In many cases, however, only the digitized parts of processes are directly captured by process-aware information sys-tems, whereas manual activities often leave blind spots in the process analysis. While video data can contain valuable process-related information that is not captured in information systems, a standardized approach to extracting event logs from unstructured video data remains lacking. To solve this problem and facilitate the systematic usage of video data in process mining, we have designed the ViProMiRA, a reference architecture that bridges the gap between computer vision and process mining. The various evaluation activities in our design science research process ensure that the proposed ViProMiRA allows flexible, use case-driven, and context-specific instantiations. Our results also show that a prototypical implementation of the ViProMiRA is capable of automatically extracting more than 70% of the process-relevant events from a real-world video dataset in a supervised learning scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Data mining

Automatic seizure detection using the pulse transit time

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arXiv 2021年

作者： Fiege, Eric Houta, Salima Bisgin, Pinar Surges, Rainer Howar, Falk Fraunhofer Institute for Software and Systems Engineering Dortmund Germany Department of Epileptology University of Bonn Medical Center Bonn Germany TU Dortmund University Dortmund Germany

Documentation of epileptic seizures plays an essential role in planning medical therapy. Solutions for automated epileptic seizure detection can help improve the current problem of incomplete and erroneous manual documentation of epileptic seizures. In recent years, a number of wearable sensors have been tested for this purpose. However, detecting seizures with subtle symptoms remains difficult and current solutions tend to have a high false alarm rate. Seizures can also affect the patient’s arterial blood pressure, which has not yet been studied for detection with sensors. The pulse transit time (PTT) provides a noninvasive estimate of arterial blood pressure. It can be obtained by using to two sensors, which are measuring the time differences between arrivals of the pulse waves. Due to separated time chips a clock drift emerges, which is strongly influencing the PTT. In this work, we present an algorithm which responds to alterations in the PTT, considering the clock drift and enabling the noninvasive monitoring of blood pressure alterations using separated sensors. Furthermore we investigated whether seizures can be detected using the PTT. Our results indicate that using the algorithm, it is possible to detect seizures with a Random Forest. Using the PTT along with other signals in a multimodal approach, the detection of seizures with subtle symptoms could thereby be improved. © 2021, CC BY.

关键词： Neurophysiology

Evaluating the effectiveness of systems and software engineering methods, processes and tools for use in defense programs

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Evaluating the effectiveness of systems and software enginee...

Annual IEEE systems Conference

作者： Michael Pennotti Richard Turner Forrest Shull School of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA Fraunhofer Center of Experimental Software Engineering University of Maryland College Park MD USA

The systems engineering research center university affiliated research center (SERC-UARC) at Stevens Institute has been tasked to evaluate the effectiveness of the systems and software engineering processes, methods and tools (MPTs) used in US department of defense acquisition and development programs. This paper presents the selection and evaluation process, describes its evolution based on changing sponsor needs, and presents additional information on the characterization of MPTs for evaluation.

关键词： software engineering systems engineering and theory USA Councils Best practices Maintenance engineering Design engineering Educational institutions Costs Engines Weapons

Fairness, integrity, and privacy in a scalable Blockchain-based federated learning system

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arXiv 2021年

作者： Rückel, Timon Sedlmeir, Johannes Hofmann, Peter University of Bayreuth Bayreuth Germany FIM Research Center University of Bayreuth Bayreuth Germany Project Group Business & Information Systems Engineering of the Fraunhofer FIT Bayreuth Germany

Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients’ models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent *** Codes 68T01, 68M14 © 2021, CC BY-NC-ND.

关键词： Blockchain