• Convergence Security Journal
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• v.22 no.3
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• pp.87-99
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• 2022

Cyber attacks have become more difficult to detect and track as sophisticated and advanced APT attacks increase. System providence graphs provide analysts of cyber security with techniques to determine the origin of attacks. Various system provenance graph techniques have been studied to reveal the origin of penetration against cyber attacks. In this study, we investigated various system provenance graph techniques and described about data collection and analysis techniques. In addition, based on the results of our survey, we presented some future research directions.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.77-86
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• 2018

For the past few years, KISTI has been servicing an online simulation execution platform, called EDISON, allowing users to conduct simulations on various scientific applications supplied by diverse computational science and engineering disciplines. Typically, these simulations accompany large-scale computation and accordingly produce a huge volume of output data. One critical issue arising when conducting those simulations on an online platform stems from the fact that a number of users simultaneously submit to the platform their simulation requests (or jobs) with the same (or almost unchanging) input parameters or files, resulting in charging a significant burden on the platform. In other words, the same computing jobs lead to duplicate consumption computing and storage resources at an undesirably fast pace. To overcome excessive resource usage by such identical simulation requests, in this paper we introduce a novel framework, called IceSheet, to efficiently manage simulation data based on execution metadata, that is, provenance. The IceSheet framework captures and stores each provenance associated with a conducted simulation. The collected provenance records are utilized for not only inspecting duplicate simulation requests but also performing search on existing simulation results via an open-source search engine, ElasticSearch. In particular, this paper elaborates on the core components in the IceSheet framework to support the search and reuse on the stored simulation results. We implemented as prototype the proposed framework using the engine in conjunction with the online simulation execution platform. Our evaluation of the framework was performed on the real simulation execution-provenance records collected on the platform. Once the prototyped IceSheet framework fully functions with the platform, users can quickly search for past parameter values entered into desired simulation software and receive existing results on the same input parameter values on the software if any. Therefore, we expect that the proposed framework contributes to eliminating duplicate resource consumption and significantly reducing execution time on the same requests as previously-executed simulations.

• The Journal of Bigdata
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• v.4 no.2
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• pp.127-139
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• 2019

Research Data Management (RDM) is a system that encompasses people, policies, resources and technologies that provide and support directions in producing, collecting, using, and preserving research data. RDMs consist of a wide range of activities, including supporting the creation of data management plans (DMPs), building data collections and repositories, and digital preservation and distribution. In advanced countries, systems for RDMs and related organizations are well organized and functioning, but in Korea, the management system is insufficient due to low level of data awareness. In this paper, we propose a plan to establish a research data management system suitable for the reality. In particular, it is important to reflect in RDM that the construction of big data platforms for the collection and management of big data in each field and organization is increasing rapidly. Also, we will discuss how to provide data provision and researchers' data sovereignty using blockchain technology, and propose a P2P-based decentralized RDM scheme.